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This paper describes a system for indexing acoustic feature vectors for large-scale speaker search using random projections. Given one or more target feature vectors, large-scale speaker search enables returning similar vectors (in a nearest-neighbors fashion) in sublinear time. The speaker feature space is comprised of i-vectors, derived from Gaussian Mixture Model supervectors. The index and search algorithm is derived from locality sensitive hashing with novel approaches in neighboring bin approximation for improving the miss rate at specified false alarm thresholds. The distance metric for determining the similarity between vectors is the cosine distance. This approach significantly reduced the search space by 70% with minimal increase in miss rate. When combined with further dimensionality reduction, a reduction of the search space by over 90% is also possible. All experiments are based on the NIST SRE 2010 evaluation.