diff --git a/notebooks/QuickStart.ipynb b/notebooks/QuickStart.ipynb
index 9777e83..026ec2c 100644
--- a/notebooks/QuickStart.ipynb
+++ b/notebooks/QuickStart.ipynb
@@ -85,15 +85,7 @@
    "execution_count": null,
    "id": "3cd0d138",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Enter JSON string: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "! adb config create --active --from-json"
    ]
@@ -307,23 +299,28 @@
    "outputs": [],
    "source": [
     "from aperturedb.CommonLibrary import create_connector, execute_query\n",
-    "from aperturedb.NotebookHelpers import display\n",
+    "from aperturedb.NotebookHelpers import display as nbd\n",
+    "from IPython.display import display\n",
     "import clip\n",
     "import torch\n",
     "\n",
     "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
     "model, preprocess = clip.load(\"ViT-B/16\", device=device)\n",
     "\n",
-    "search_tokens = clip.tokenize([f\"a photo of bread\"]).to(device)\n",
+    "search_tokens = clip.tokenize([f\"a photo of bread on plate\"]).to(device)\n",
     "search_embeddings = model.encode_text(search_tokens)\n",
     "\n",
     "query = [{\n",
     "    \"FindDescriptor\": {\n",
     "        \"set\": \"ViT-B/16\", # Name of descriptor set to use\n",
-    "        \"k_neighbors\": 5, # How many results to return\n",
+    "        \"k_neighbors\": 10, # How many results to return\n",
     "        \"distances\": True, # Also return distances (as _distance property)\n",
     "        \"blobs\": False, # Don't return vectors of matching descriptors\n",
-    "        \"_ref\": 1 # forward reference\n",
+    "        \"_ref\": 1, # forward reference,\n",
+    "        \"metric\": \"CS\",\n",
+    "        \"results\": {\n",
+    "           \"list\": [\"_uniqueid\"]\n",
+    "        }\n",
     "    }\n",
     "}, {\n",
     "    \"FindImage\": {\n",
@@ -332,7 +329,8 @@
     "            \"ref\": 1 # backward reference\n",
     "        },\n",
     "        \"results\": {\n",
-    "            \"all_properties\": True # Return all properties\n",
+    "            \"list\": [\"_uniqueid\"], # Return all properties\n",
+    "            \"group_by_source\": True\n",
     "        }\n",
     "    }\n",
     "}]\n",
@@ -351,7 +349,14 @@
     "\n",
     "# Check if the query was successful\n",
     "if result == 0:\n",
-    "  display(blobs)\n"
+    "  #Here we reorder the images based on the distances before displaying them\n",
+    "  # based on decreasing distance from the input vector.\n",
+    "  descriptors = response[0][\"FindDescriptor\"][\"entities\"]\n",
+    "  images = response[1][\"FindImage\"][\"entities\"]\n",
+    "  for d in descriptors:\n",
+    "    bi = images[d[\"_uniqueid\"]][0][\"_blob_index\"]\n",
+    "    display(d[\"_distance\"], bi)\n",
+    "    nbd([blobs[bi]])\n"
    ]
   },
   {