diff --git a/python/python/lancedb/util.py b/python/python/lancedb/util.py
index 6a5be99db..f582be7b4 100644
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -307,13 +307,19 @@ def infer_vector_column_name(
         # FTS queries do not require a vector column
         return None
 
-    if query is not None or query_type == "hybrid":
-        try:
-            vector_column_name = inf_vector_column_query(
-                schema, dim=_query_vector_dim(query)
-            )
-        except Exception as e:
-            raise e
+    if query is None and query_type != "hybrid":
+        # No vector search was requested (e.g. a plain scan), so there's
+        # nothing to infer.
+        return None
+
+    vector_column_name = inf_vector_column_query(schema, dim=_query_vector_dim(query))
+
+    if vector_column_name is None:
+        raise ValueError(
+            "No vector column found in the schema. Please specify the "
+            "vector column name explicitly via the `vector_column_name` "
+            "parameter."
+        )
 
     return vector_column_name
 
diff --git a/python/python/tests/test_util.py b/python/python/tests/test_util.py
index 7cb0ce448..a9b66b2dd 100644
--- a/python/python/tests/test_util.py
+++ b/python/python/tests/test_util.py
@@ -924,3 +924,23 @@ def test_sanitize_data_stream():
 
     with pytest.raises(ValueError):
         next(output)
+
+
+def test_infer_vector_column_raises_clear_error(tmp_path):
+    """Regression: querying a table with no inferable vector column should raise
+    a clear ValueError, not a cryptic TypeError (issue #1653).
+
+    Previously, inf_vector_column_query silently returned None which then caused
+    a confusing TypeError deep in schema lookup. The fix adds a ValueError guard
+    so the user gets a direct, actionable error message.
+    """
+    db = lancedb.connect(tmp_path)
+    table = db.create_table(
+        "no_vec",
+        data=[{"id": 1, "text": "hello"}, {"id": 2, "text": "world"}],
+    )
+
+    with pytest.raises(ValueError, match="vector"):
+        # Plain vector search on a table with no vector column should raise
+        # a clear ValueError, not a cryptic TypeError.
+        table.search([1.0, 2.0]).to_list()