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Near neighbor search in high dimensional spaces is useful in many applications. Existing techniques solve this problem efficiently only for the approximate cases. These solutions are designed to solve r-near neighbor queries for a fixed query range or for a set of query ranges with probabilistic guarantees, and then extended for nearest neighbor queries. Solutions supporting a set of query ranges suffer from prohibitive space cost. There are many applications which are quality sensitive and need to efficiently and accurately support near neighbor queries for all query ranges. In this paper, we propose a novel indexing and querying scheme called Spatial Intersection and Metric Pruning (SIMP). It efficiently supports r-near neighbor queries in very high dimensional spaces for all query ranges with 100% quality guarantee and with practical storage costs. Our empirical studies on three real datasets having dimensions between 32 and 256 and sizes up to 10 million show a superior performance of SIMP over LSH, Multi-Probe LSH, LSB tree, and iDistance. Our scalability tests on real datasets having as many as 100 million points of dimensions up to 256 establish that SIMP scales linearly with query range, dataset dimension, and dataset size.