diff --git a/studio/backend/core/rag/retrieval.py b/studio/backend/core/rag/retrieval.py
index e6eb45c276..d963737213 100644
--- a/studio/backend/core/rag/retrieval.py
+++ b/studio/backend/core/rag/retrieval.py
@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import re
 from dataclasses import dataclass
 
 from utils.rag.config import (
@@ -16,6 +17,31 @@ from utils.rag.config import (
 
 from . import bm25, embeddings, vector_store
 
+# Match "Figure 1", "Figure 1.2", "Figure B.1", "Table 4", "Fig. 5" anywhere
+# in the query. Used to inject a third retrieval source that directly looks
+# up chunks anchored by these references — dense vectors don't preserve
+# figure numbers, so without this an exact-numbered query gets out-ranked
+# by chunks describing other figures that share more vocabulary with the
+# question.
+_FIGURE_REF_RE = re.compile(
+    r"\b(Figure|Fig\.|Table|Tab\.)\s+([A-Z]?\.?\d+(?:\.\d+)?)\b",
+    re.IGNORECASE,
+)
+
+
+def _extract_figure_refs(query: str) -> list[str]:
+    """Return normalized 'Figure N' / 'Table N' references found in query."""
+    refs: list[str] = []
+    seen: set[str] = set()
+    for m in _FIGURE_REF_RE.finditer(query):
+        head = m.group(1).lower()
+        label = "Figure" if head.startswith("fig") else "Table"
+        ref = f"{label} {m.group(2)}"
+        if ref not in seen:
+            seen.add(ref)
+            refs.append(ref)
+    return refs
+
 
 @dataclass(frozen = True)
 class Hit:
@@ -33,6 +59,67 @@ def retrieve_bm25(scope: str, query: str, k: int | None = None) -> list[Hit]:
     return [Hit(chunk_id = cid, score = s) for cid, s in bm25.search(scope, query, limit)]
 
 
+def retrieve_figure_refs(
+    scope: str,
+    query: str,
+    *,
+    k: int = 5,
+    document_ids: list[str] | None = None,
+) -> list[Hit]:
+    """Look up chunks anchored at a 'Figure N:' / 'Table N:' caption that
+    the query references. Returns at most ``k`` hits — usually 0 or 1.
+
+    Chunks produced by the figure-boundary chunker start with the literal
+    caption, so a SQL prefix match is enough; we don't need full-text
+    search here.
+    """
+    refs = _extract_figure_refs(query)
+    if not refs:
+        return []
+
+    from .db import get_rag_connection
+
+    placeholders_docs = ""
+    params: list = [scope]
+    if document_ids:
+        placeholders_docs = (
+            f" AND document_id IN ({','.join('?' * len(document_ids))})"
+        )
+        params.extend(document_ids)
+
+    like_clauses: list[str] = []
+    for ref in refs:
+        # Match "Figure 1:" and "Figure 1." (period-terminated captions).
+        like_clauses.append(
+            "json_extract(payload_json, '$.text') LIKE ?"
+            " OR json_extract(payload_json, '$.text') LIKE ?"
+        )
+        params.extend([f"{ref}:%", f"{ref}.%"])
+
+    sql = (
+        "SELECT chunk_id, document_id, chunk_index, kind"
+        " FROM rag_vectors"
+        f" WHERE scope = ?{placeholders_docs}"
+        " AND kind = 'text'"
+        f" AND ({' OR '.join(like_clauses)})"
+        f" LIMIT {int(k)}"
+    )
+
+    out: list[Hit] = []
+    with get_rag_connection() as conn:
+        for row in conn.execute(sql, params):
+            out.append(
+                Hit(
+                    chunk_id = row[0],
+                    score = 1.0,
+                    document_id = row[1],
+                    chunk_index = row[2],
+                    kind = row[3] or "text",
+                )
+            )
+    return out
+
+
 def retrieve_dense(
     scope: str,
     query: str,
@@ -121,8 +208,12 @@ def retrieve_hybrid(
         document_ids = document_ids,
         embedder_model = embedder_model,
     )
+    rankings = [bm25_hits, dense_hits]
+    fig_hits = retrieve_figure_refs(scope, query, document_ids = document_ids)
+    if fig_hits:
+        rankings.append(fig_hits)
     return _rrf_fuse(
-        [bm25_hits, dense_hits],
+        rankings,
         rrf_k = RAG_RRF_K,
         top_k = k or RAG_TOP_K_HYBRID,
     )