In the modern context, the similarity is determined by content preserving stimuli, retrieval of relevant ‘nearest neighbor’s objects and the way similar objects are pursued. Current similarity search in hamming-space based strategies finds all the data objects within a threshold hamming-distance for a user query. Though, the number of computations for distance and candidate generation are key concerns from the many years. The hamming-space paradigm extends the range of alternatives for an optimized search experience. A novel ‘counting based similarity search strategy is proposed, with an improved hamming-space, e.g. optimized candidate generation and verification function. The strategy adapts towards the lesser set of user query dimensions and subsequently constraints the hamming-space computations with each data objects, driven by generated statistics. The extensive evaluation asserts that the proposed ‘counting based approach can be combined with any pigeonhole principle-based similarity search to further improve its performance.