diff --git a/python/semantic_kernel/memory/volatile_memory_store.py b/python/semantic_kernel/memory/volatile_memory_store.py
index dd1a631d0614..7a57862f1edd 100644
--- a/python/semantic_kernel/memory/volatile_memory_store.py
+++ b/python/semantic_kernel/memory/volatile_memory_store.py
@@ -251,6 +251,9 @@ async def get_nearest_matches(
         # Get all the records in the collection
         memory_records = list(self._store[collection_name].values())
 
+        if not memory_records:
+            return []
+
         # Convert the collection of embeddings into a numpy array (stacked)
         embeddings = array([x._embedding for x in memory_records], dtype=float)
         embeddings = embeddings.reshape(embeddings.shape[0], -1)
diff --git a/python/tests/unit/memory/test_volatile_memory_store.py b/python/tests/unit/memory/test_volatile_memory_store.py
new file mode 100644
index 000000000000..3b3f6323ff22
--- /dev/null
+++ b/python/tests/unit/memory/test_volatile_memory_store.py
@@ -0,0 +1,39 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import numpy as np
+
+from semantic_kernel.memory.memory_record import MemoryRecord
+from semantic_kernel.memory.volatile_memory_store import VolatileMemoryStore
+
+
+async def test_get_nearest_matches_empty_collection():
+    store = VolatileMemoryStore()
+    await store.create_collection("test")
+    result = await store.get_nearest_matches("test", np.array([1.0, 2.0]), limit=3)
+    assert result == []
+
+
+async def test_get_nearest_matches_missing_collection():
+    store = VolatileMemoryStore()
+    result = await store.get_nearest_matches("missing", np.array([1.0, 2.0]), limit=3)
+    assert result == []
+
+
+async def test_get_nearest_matches_with_records():
+    store = VolatileMemoryStore()
+    await store.create_collection("test")
+    await store.upsert(
+        "test",
+        MemoryRecord(
+            is_reference=False,
+            external_source_name=None,
+            id="first",
+            description="first record",
+            text="hello",
+            additional_metadata=None,
+            embedding=np.array([1.0, 0.0]),
+        ),
+    )
+    result = await store.get_nearest_matches("test", np.array([1.0, 0.0]), limit=3)
+    assert len(result) == 1
+    assert result[0][0].id == "first"