diff --git a/docs/web/docs.json b/docs/web/docs.json
index 9c9468a3b..54203f1a0 100644
--- a/docs/web/docs.json
+++ b/docs/web/docs.json
@@ -350,6 +350,7 @@
             "pages": [
               "enterprise/index",
               "enterprise/architecture",
+              "enterprise/distributed-search",
               "enterprise/deployment/index",
               "deployment/storage",
               "deployment/monitoring",
diff --git a/docs/web/enterprise/architecture.mdx b/docs/web/enterprise/architecture.mdx
index c47dcbd2e..8f887c142 100644
--- a/docs/web/enterprise/architecture.mdx
+++ b/docs/web/enterprise/architecture.mdx
@@ -18,3 +18,6 @@ run on machines that can be replaced without any of them owning the data.
 
 [Architecture](/architecture) covers this alongside the embedded form, which is
 the comparison most readers want first.
+
+See [Distributed search](/enterprise/distributed-search) for how Enterprise
+distributes index execution and table-data reads.
diff --git a/docs/web/enterprise/distributed-search.mdx b/docs/web/enterprise/distributed-search.mdx
new file mode 100644
index 000000000..ebac960ba
--- /dev/null
+++ b/docs/web/enterprise/distributed-search.mdx
@@ -0,0 +1,33 @@
+---
+title: "Distributed search"
+sidebarTitle: "Distributed search"
+description: "How LanceDB Enterprise distributes index execution and table-data reads."
+icon: "network-wired"
+availability:
+  oss: unavailable
+  enterprise: available
+  summary: >-
+    LanceDB Enterprise distributes index execution and table-data reads across
+    plan executors. Embedded LanceDB executes searches in the application process.
+---
+
+Distributed search spreads index execution, table-data reads, and caching across
+multiple serving machines in LanceDB Enterprise. Query nodes plan queries and
+combine results. Plan executors cache index structures and table data and execute
+the remote work. See [Enterprise architecture](/enterprise/architecture).
+
+## Embedded search {#embedded-search}
+
+LanceDB OSS runs inside your application process. It searches indexes and reads
+table data using that process's resources, including its index cache. Reading a
+table from object storage does not by itself distribute query execution across
+machines.
+
+## Search interfaces {#search-interfaces}
+
+The shared search guides describe the query APIs and index settings:
+
+- [Vector search](/search/vector-search) and [vector indexes](/indexing/vector-index).
+- [Full-text search](/search/full-text-search) and [FTS indexes](/indexing/fts-index).
+- [Filtering](/search/filtering) and [scalar indexes](/indexing/scalar-index).
+- [Query plans](/search/explain) for inspecting execution and runtime measurements.
diff --git a/docs/web/indexing/vector-index.mdx b/docs/web/indexing/vector-index.mdx
index 2742a992f..b26e0c295 100644
--- a/docs/web/indexing/vector-index.mdx
+++ b/docs/web/indexing/vector-index.mdx
@@ -384,7 +384,7 @@ The inner workings of RaBitQ quantization are rather mathematically dense. It ge
 applying a uniformly random, approximately distance-preserving orthogonal transformation of the vertices of the $d$-dimensional hypercube, 
 where $d$ is the dimensionality of the dataset. We defer the details, and an elegant theoretical error bound, to [the original paper.](https://arxiv.org/pdf/2405.12497)
 
-##### Using RaBitQ
+##### Using RaBitQ {#using-rabitq}
 Use RaBitQ quantization by selecting quantized index types ending in the suffix `RQ`. For example, call `create_index` with `index_type="IVF_RQ"`. 
 Note that when using `IVF_RQ`, the dimension of the dataset must be a multiple of `8`.
 
diff --git a/docs/web/search/vector-search.mdx b/docs/web/search/vector-search.mdx
index 3519df553..cc6ae5421 100644
--- a/docs/web/search/vector-search.mdx
+++ b/docs/web/search/vector-search.mdx
@@ -235,7 +235,7 @@ The table below summarizes the behavior of `_distance` in search results based o
 See [vector indexes](/indexing/vector-index) for index configuration. The
 following sections cover query-time settings.
 
-### Tuning `approx_mode`
+### Tuning `approx_mode` {#tuning-approx-mode}
 
 Use `approx_mode` when you want to adjust the speed/recall trade-off for approximate vector search at query time. This setting currently applies only to RQ-quantized indexes, such as `IVF_RQ`; other index types ignore it.