diff --git a/doc/DataSet.rst b/doc/DataSet.rst
index 36d8c1c..f5093e8 100644
--- a/doc/DataSet.rst
+++ b/doc/DataSet.rst
@@ -89,6 +89,14 @@
 
    Merge sourceDataSet in the current DataSet. It will update the value of points with the same identifier if overwrite is set to 1. ​To add columns instead, see the 'transformJoin' method of FluidDataSetQuery.
 
+:message kNearest:
+
+   :arg buffer: A |buffer| containing a data point to match against. The number of frames in the buffer must match the dimensionality of the DataSet.
+
+   :arg k: The number of nearest neighbours to return. The identifiers will be sorted, beginning with the nearest.
+
+   Returns the identifiers of the ``k`` points nearest to the one passed. Note that this is a brute force distance measure, and comparatively inefficient for repeated queries against large datasets. For such cases, :fluid-obj:`KDTree` will be more efficient.
+
 :message print:
 
    Post an abbreviated content of the DataSet in the window by default, but you can supply a custom action instead. 
diff --git a/doc/KDTree.rst b/doc/KDTree.rst
index 9c6d840..2252819 100644
--- a/doc/KDTree.rst
+++ b/doc/KDTree.rst
@@ -7,6 +7,8 @@
 :discussion: 
    :fluid-obj:`KDTree` facilitates efficient nearest neighbour searches of multi-dimensional data stored in a :fluid-obj:`DataSet`. 
 
+   k-d trees are most useful for *repeated* querying of a dataset, because there is a cost associated with building them. If you just need to do a single lookup then using the kNearest message of :fluid-obj:`DataSet` will probably be quicker
+   
    Whilst k-d trees can offer very good performance relative to naïve search algorithms, they suffer from something called “the curse of dimensionality” (like many algorithms for multi-dimensional data). In practice, this means that as the number of dimensions of your data goes up, the relative performance gains of a k-d tree go down.
 
 :control numNeighbours:
diff --git a/example-code/sc/DataSet.scd b/example-code/sc/DataSet.scd
index 4dc6d95..bfb966f 100644
--- a/example-code/sc/DataSet.scd
+++ b/example-code/sc/DataSet.scd
@@ -250,4 +250,29 @@ fork{
 }
 )
 
+::
+strong::Nearest Neighbour Search in a DataSet::
+
+Note: A FluidDataSet can be queried with an input point to return the nearest match to that point. Note: This feature is can be computationally expensive on a large dataset, as it needs to compute the distance of the queried point to each point in the dataset. If you need to perform multiple nearest neighbour queries on a fluid.dataset~ it is recommended to use FluidKDTree. This facility is most useful with smaller, ephemeral datasets such as those returned by FluidDataSetQuery.
+
+code::
+
+// create a small DataSet...
+f = FluidDataSet(s)
+// and fill it with a grid of data
+f.load(Dictionary.newFrom(["cols", 2, "data", Dictionary.newFrom(9.collect{|i|["item-%".format(i), [i.div(3), i.mod(3)] / 2]}.flatten(1))]))
+
+// the data looks like this
+// (item-0 -> [ 0.0, 0.0 ]) (item-1 -> [ 0.0, 0.5 ]) (item-2 -> [ 0.0, 1.0 ])
+// (item-3 -> [ 0.5, 0.0 ]) (item-4 -> [ 0.5, 0.5 ]) (item-5 -> [ 0.5, 1.0 ])
+// (item-6 -> [ 1.0, 0.0 ]) (item-7 -> [ 1.0, 0.5 ]) (item-8 -> [ 1.0, 1.0 ])
+
+// create a query buffer...
+b = Buffer.alloc(s,2)
+
+// and fill it with a point
+b.sendCollection([1,0]);
+
+// and request 9 nearest neighbours
+f.kNearest(b,9,{|x|x.postln;})
 ::
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