diff --git a/src/memory/api/search/search.py b/src/memory/api/search/search.py
index 5912547..33b9a13 100644
--- a/src/memory/api/search/search.py
+++ b/src/memory/api/search/search.py
@@ -28,6 +28,9 @@ if settings.ENABLE_RERANKING:
 
 from memory.api.search.types import SearchConfig, SearchFilters, SearchResult
 
+# Default config for when none is provided
+_DEFAULT_CONFIG = SearchConfig()
+
 logger = logging.getLogger(__name__)
 
 # Reciprocal Rank Fusion constant (k parameter)
@@ -313,6 +316,7 @@ async def search_chunks(
     limit: int = 10,
     filters: SearchFilters = {},
     timeout: int = 2,
+    config: SearchConfig = _DEFAULT_CONFIG,
 ) -> list[Chunk]:
     """
     Search chunks using embedding similarity and optionally BM25.
@@ -322,7 +326,19 @@ async def search_chunks(
 
     If HyDE is enabled, also generates a hypothetical document from the query
     and includes it in the embedding search for better semantic matching.
+
+    Enhancement flags in config override global settings when set:
+    - useBm25: Enable BM25 lexical search
+    - useHyde: Enable HyDE query expansion
+    - useReranking: Enable cross-encoder reranking
+    - useQueryExpansion: Enable synonym/abbreviation expansion
     """
+    # Resolve enhancement flags: config overrides global settings
+    use_bm25 = config.useBm25 if config.useBm25 is not None else settings.ENABLE_BM25_SEARCH
+    use_hyde = config.useHyde if config.useHyde is not None else settings.ENABLE_HYDE_EXPANSION
+    use_reranking = config.useReranking if config.useReranking is not None else settings.ENABLE_RERANKING
+    use_query_expansion = config.useQueryExpansion if config.useQueryExpansion is not None else True
+
     # Search for more candidates than requested, fuse scores, then return top N
     # This helps find results that rank well in one method but not the other
     internal_limit = limit * CANDIDATE_MULTIPLIER
@@ -331,17 +347,20 @@ async def search_chunks(
     query_text = " ".join(
         c for chunk in data for c in chunk.data if isinstance(c, str)
     )
-    expanded_query = expand_query(query_text)
 
-    # If query was expanded, use expanded version for search
-    if expanded_query != query_text:
-        logger.debug(f"Query expansion: '{query_text}' -> '{expanded_query}'")
-        search_data = [extract.DataChunk(data=[expanded_query])]
+    if use_query_expansion:
+        expanded_query = expand_query(query_text)
+        # If query was expanded, use expanded version for search
+        if expanded_query != query_text:
+            logger.debug(f"Query expansion: '{query_text}' -> '{expanded_query}'")
+            search_data = [extract.DataChunk(data=[expanded_query])]
+        else:
+            search_data = list(data)  # Copy to avoid modifying original
     else:
-        search_data = list(data)  # Copy to avoid modifying original
+        search_data = list(data)
 
     # Apply HyDE expansion if enabled
-    if settings.ENABLE_HYDE_EXPANSION:
+    if use_hyde:
         # Only expand queries with 4+ words (short queries are usually specific enough)
         if len(query_text.split()) >= 4:
             try:
@@ -361,7 +380,7 @@ async def search_chunks(
 
     # Run BM25 search if enabled
     bm25_scores: dict[str, float] = {}
-    if settings.ENABLE_BM25_SEARCH:
+    if use_bm25:
         try:
             bm25_scores = await search_bm25_chunks(
                 data, modalities, internal_limit, filters, timeout
@@ -378,7 +397,7 @@ async def search_chunks(
     # Sort by score and take top results
     # If reranking is enabled, fetch more candidates for the reranker to work with
     sorted_ids = sorted(fused_scores.keys(), key=lambda x: fused_scores[x], reverse=True)
-    if settings.ENABLE_RERANKING:
+    if use_reranking:
         fetch_limit = limit * RERANK_CANDIDATE_MULTIPLIER
     else:
         fetch_limit = limit
@@ -421,7 +440,7 @@ async def search_chunks(
         apply_source_boosts(chunks, set())
 
     # Rerank using cross-encoder for better precision
-    if settings.ENABLE_RERANKING and chunks and query_text.strip():
+    if use_reranking and chunks and query_text.strip():
         try:
             chunks = await rerank_chunks(
                 query_text, chunks, model=settings.RERANK_MODEL, top_k=limit
@@ -451,7 +470,7 @@ async def search(
     data: list[extract.DataChunk],
     modalities: set[str] = set(),
     filters: SearchFilters = {},
-    config: SearchConfig = SearchConfig(),
+    config: SearchConfig = _DEFAULT_CONFIG,
 ) -> list[SearchResult]:
     """
     Search across knowledge base using text query and optional files.
@@ -472,6 +491,7 @@ async def search(
         config.limit,
         filters,
         config.timeout,
+        config,
     )
     if settings.ENABLE_SEARCH_SCORING and config.useScores:
         chunks = await scorer.rank_chunks(data[0].data[0], chunks, min_score=0.3)
diff --git a/src/memory/api/search/types.py b/src/memory/api/search/types.py
index 00ee7ff..f3bc63a 100644
--- a/src/memory/api/search/types.py
+++ b/src/memory/api/search/types.py
@@ -83,6 +83,12 @@ class SearchConfig(BaseModel):
     previews: bool = False
     useScores: bool = False
 
+    # Optional enhancement flags (None = use global setting from env)
+    useBm25: Optional[bool] = None
+    useHyde: Optional[bool] = None
+    useReranking: Optional[bool] = None
+    useQueryExpansion: Optional[bool] = None
+
     def model_post_init(self, __context) -> None:
         # Enforce reasonable limits
         if self.limit < 1: