We focus on the recently introduced nearest neighbor based entropy estimator from Kraskov, Stögbauer and Grassberger (KSG), the nearest neighbor search of which is performed by the so called box assisted algorithm. We compare the performance of KSG with respect to three spatial indexing methods: box-assisted, k-D trie and projection method, on a problem of mutual information estimation of a variety of pdfs and dimensionalities. We conclude that the k-D trie method is significantly faster then box-assisted search in fixed-mass and fixed-radius neighborhood searches in higher dimensions. The projection method is much slower than both alternatives and not recommended for practical use.

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