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In the first two years of Gravitational Wave (GW) Astronomy, half a dozencompact binary coalescences (CBCs) have been detected. As the sensitivities andbandwidths of the detectors improve and new detectors join the network, manymore sources are expected to be detected. The goal will not only be to find asmany sources as possible in the data but to understand the dynamics of thesources much more precisely. Standard searches are currently restricted to asmaller parameter space which assumes aligned spins. Construction of a largerand denser parameter space, and optimising the resultant increase in falsealarms, pose a serious computational challenge. We present here a two-stagehierarchical strategy to search for CBCs in data from a network of detectorsand demonstrate the computational advantage in real life scenario byintroducing it in the standard {\tt PyCBC} pipeline with the usual restrictedparameter space. With this strategy, in simulated data containing stationaryGaussian noise, we obtain a computational gain of $\sim 20$ over the flatsearch. In real data, we expect the computational gain up to a factor of few.This saving in the computational effort will, in turn, allow us to search forprecessing binaries. Freeing up computation time for the regular analyses willprovide more options to search for sources of different kinds and to fulfil thenever-ending urge for extracting more science out of the data with limitedresources.

Hierarchical search strategy for the efficient detection of gravitational waves from non-precessing coalescing compact binaries with aligned-spins