Abstract: Fast searching of content in large motion databases is essential for efficient motion analysis and synthesis. In this work we demonstrate that identifying locally similar regions in human motion data can be practical even for huge databases, if medium-dimensional (15--90 dimensional) feature sets are used for kd-tree-based nearest-neighbor-searches. On the basis of kd-tree-based local neighborhood searches we devise a novel fast method for global similarity searches. We show that knn-searches can be used efficiently within the problems of (a) "numerical and logical similarity searches," (b) reconstruction of motions from sparse marker sets, and (c) building so called "fat graphs," tasks for which previously algorithms with preprocessing time quadratic in the size of the database and thus only applicable to small collections of motions had been presented. We test our techniques on the two largest freely available motion capture databases, the CMU and HDM05 motion databases comprising more than 750 min of motion capture data proving that our approach is not only theoretically applicable but also solves the problem of fast similarity searches in huge motion databases in practice.