diff --git a/environment.yml b/environment.yml
index bd7f9f59..67535adc 100644
--- a/environment.yml
+++ b/environment.yml
@@ -10,4 +10,6 @@ dependencies:
   - fastapi
   - uvicorn
   - pyyaml
-  - pytest
\ No newline at end of file
+  - pytest
+  - pillow
+  - torchvision
\ No newline at end of file
diff --git a/src/main.py b/src/main.py
index 3c9fa2a5..b8b03e06 100644
--- a/src/main.py
+++ b/src/main.py
@@ -8,7 +8,7 @@ import uvicorn
 from fastapi import FastAPI
 
 # Internal Packages
-from search_type import asymmetric, symmetric_ledger
+from search_type import asymmetric, symmetric_ledger, image_search
 from utils.helpers import get_from_dict
 from utils.cli import cli
 
@@ -50,6 +50,22 @@ def search(q: str, n: Optional[int] = 5, t: Optional[str] = None):
         # collate and return results
         return symmetric_ledger.collate_results(hits, transactions, results_count)
 
+    if (t == 'image' or t == None) and image_search_enabled:
+        # query transactions
+        hits = image_search.query_images(
+            user_query,
+            image_embeddings,
+            image_encoder,
+            results_count,
+            args.verbose)
+
+        # collate and return results
+        return image_search.collate_results(
+            hits,
+            image_names,
+            image_config['input-directory'],
+            results_count)
+
     else:
         return {}
 
@@ -80,6 +96,16 @@ def regenerate(t: Optional[str] = None):
             regenerate=True,
             verbose=args.verbose)
 
+    if (t == 'image' or t == None) and image_search_enabled:
+        # Extract Images, Generate Embeddings
+        global image_embeddings
+        global image_names
+        image_names, image_embeddings, _ = image_search.setup(
+            pathlib.Path(image_config['input-directory']),
+            pathlib.Path(image_config['embeddings-file']),
+            regenerate=True,
+            verbose=args.verbose)
+
     return {'status': 'ok', 'message': 'regeneration completed'}
 
 
@@ -112,5 +138,16 @@ if __name__ == '__main__':
             args.regenerate,
             args.verbose)
 
+    # Initialize Image Search
+    image_config = get_from_dict(args.config, 'content-type', 'image')
+    image_search_enabled = False
+    if image_config and 'input-directory' in image_config:
+        image_search_enabled = True
+        image_names, image_embeddings, image_encoder = image_search.setup(
+            pathlib.Path(image_config['input-directory']),
+            pathlib.Path(image_config['embeddings-file']),
+            args.regenerate,
+            args.verbose)
+
     # Start Application Server
     uvicorn.run(app)
diff --git a/src/search_type/image-search.py b/src/search_type/image_search.py
similarity index 63%
rename from src/search_type/image-search.py
rename to src/search_type/image_search.py
index d62f4239..88b69f83 100644
--- a/src/search_type/image-search.py
+++ b/src/search_type/image_search.py
@@ -1,31 +1,38 @@
-from sentence_transformers import SentenceTransformer, util
-from PIL import Image
-import torch
+# Standard Packages
 import argparse
 import pathlib
 import copy
 
+# External Packages
+from sentence_transformers import SentenceTransformer, util
+from PIL import Image
+import torch
+
+# Internal Packages
+from utils.helpers import get_absolute_path, resolve_absolute_path
+
 
 def initialize_model():
     # Initialize Model
     torch.set_num_threads(4)
-    top_k = 3
     model = SentenceTransformer('clip-ViT-B-32')  #Load the CLIP model
-    return model, top_k
+    return model
 
 
 def extract_entries(image_directory, verbose=False):
+    image_directory = resolve_absolute_path(image_directory, strict=True)
     image_names = list(image_directory.glob('*.jpg'))
     if verbose:
         print(f'Found {len(image_names)} images in {image_directory}')
     return image_names
 
 
-def compute_embeddings(image_names, model, embeddings_file, verbose=False):
+def compute_embeddings(image_names, model, embeddings_file, regenerate=False, verbose=False):
     "Compute (and Save) Embeddings or Load Pre-Computed Embeddings"
+    image_embeddings = None
 
     # Load pre-computed embeddings from file if exists
-    if embeddings_file.exists():
+    if embeddings_file.exists() and not regenerate:
         image_embeddings = torch.load(embeddings_file)
         if verbose:
             print(f"Loaded pre-computed embeddings from {embeddings_file}")
@@ -46,10 +53,10 @@ def compute_embeddings(image_names, model, embeddings_file, verbose=False):
     return image_embeddings
 
 
-def search(query, image_embeddings, model, count=3, verbose=False):
+def query_images(query, image_embeddings, model, count=3, verbose=False):
     # Set query to image content if query is a filepath
-    if pathlib.Path(query).expanduser().is_file():
-        query_imagepath = pathlib.Path(query).expanduser().resolve(strict=True)
+    if pathlib.Path(query).is_file():
+        query_imagepath = resolve_absolute_path(pathlib.Path(query), strict=True)
         query = copy.deepcopy(Image.open(query_imagepath))
         if verbose:
             print(f"Find Images similar to Image at {query_imagepath}")
@@ -68,6 +75,8 @@ def search(query, image_embeddings, model, count=3, verbose=False):
 
 
 def render_results(hits, image_names, image_directory, count):
+    image_directory = resolve_absolute_path(image_directory, strict=True)
+
     for hit in hits[:count]:
         print(image_names[hit['corpus_id']])
         image_path = image_directory.joinpath(image_names[hit['corpus_id']])
@@ -75,28 +84,44 @@ def render_results(hits, image_names, image_directory, count):
             img.show()
 
 
+def collate_results(hits, image_names, image_directory, count=5):
+    image_directory = resolve_absolute_path(image_directory, strict=True)
+    return [
+        {
+            "Entry": image_directory.joinpath(image_names[hit['corpus_id']]),
+            "Score": f"{hit['score']:.3f}"
+        }
+        for hit
+        in hits[0:count]]
+
+
+def setup(image_directory, embeddings_file, regenerate=False, verbose=False):
+    # Initialize Model
+    model = initialize_model()
+
+    # Extract Entries
+    image_directory = resolve_absolute_path(image_directory, strict=True)
+    image_names = extract_entries(image_directory, verbose)
+
+    # Compute or Load Embeddings
+    embeddings_file = resolve_absolute_path(embeddings_file)
+    image_embeddings = compute_embeddings(image_names, model, embeddings_file, regenerate=regenerate, verbose=verbose)
+
+    return image_names, image_embeddings, model
+
+
 if __name__ == '__main__':
     # Setup Argument Parser
     parser = argparse.ArgumentParser(description="Semantic Search on Images")
     parser.add_argument('--image-directory', '-i', required=True, type=pathlib.Path, help="Image directory to query")
-    parser.add_argument('--embeddings-file', '-e', default='embeddings.pt', type=pathlib.Path, help="File to save/load model embeddings to/from. Default: ./embeddings.pt")
+    parser.add_argument('--embeddings-file', '-e', default='image_embeddings.pt', type=pathlib.Path, help="File to save/load model embeddings to/from. Default: ./embeddings.pt")
+    parser.add_argument('--regenerate', action='store_true', default=False, help="Regenerate embeddings of Images in Image Directory . Default: false")
     parser.add_argument('--results-count', '-n', default=5, type=int, help="Number of results to render. Default: 5")
     parser.add_argument('--interactive', action='store_true', default=False, help="Interactive mode allows user to run queries on the model. Default: true")
     parser.add_argument('--verbose', action='store_true', default=False, help="Show verbose conversion logs. Default: false")
     args = parser.parse_args()
 
-    # Resolve file, directory paths in args to absolute paths
-    embeddings_file = args.embeddings_file.expanduser().resolve()
-    image_directory = args.image_directory.expanduser().resolve(strict=True)
-
-    # Initialize Model
-    model, count = initialize_model()
-
-    # Extract Entries
-    image_names = extract_entries(image_directory, args.verbose)
-
-    # Compute or Load Embeddings
-    image_embeddings = compute_embeddings(image_names, model, embeddings_file, args.verbose)
+    image_names, image_embeddings, model = setup(args.image_directory, args.embeddings_file, regenerate=args.regenerate)
 
     # Run User Queries on Entries in Interactive Mode
     while args.interactive:
@@ -105,8 +130,8 @@ if __name__ == '__main__':
         if user_query == "exit":
             exit(0)
 
-        # query notes
-        hits = search(user_query, image_embeddings, model, args.results_count, args.verbose)
+        # query images
+        hits = query_images(user_query, image_embeddings, model, args.results_count, args.verbose)
 
         # render results
-        render_results(hits, image_names, image_directory, count=args.results_count)
+        render_results(hits, image_names, args.image_directory, count=args.results_count)
diff --git a/src/utils/cli.py b/src/utils/cli.py
index cd1519d3..6f8489a3 100644
--- a/src/utils/cli.py
+++ b/src/utils/cli.py
@@ -53,6 +53,10 @@ default_config = {
         {
             'compressed-jsonl': '.transactions.jsonl.gz',
             'embeddings-file': '.transaction_embeddings.pt'
+        },
+        'image':
+        {
+            'embeddings-file': '.image_embeddings.pt'
         }
     },
     'search-type':
@@ -61,6 +65,10 @@ default_config = {
         {
             'encoder': "sentence-transformers/msmarco-MiniLM-L-6-v3",
             'cross-encoder': "cross-encoder/ms-marco-MiniLM-L-6-v2"
+        },
+        'image':
+        {
+            'encoder': "clip-ViT-B-32"
         }
     }
 }
diff --git a/src/utils/helpers.py b/src/utils/helpers.py
index 2ed0bf36..b9f60ef0 100644
--- a/src/utils/helpers.py
+++ b/src/utils/helpers.py
@@ -9,6 +9,10 @@ def get_absolute_path(filepath):
     return str(pathlib.Path(filepath).expanduser().absolute())
 
 
+def resolve_absolute_path(filepath, strict=False):
+    return pathlib.Path(filepath).expanduser().absolute().resolve(strict=strict)
+
+
 def get_from_dict(dictionary, *args):
     '''null-aware get from a nested dictionary
        Returns: dictionary[args[0]][args[1]]... or None if any keys missing'''