Identifying mechanisms of drug action remains a fundamental impediment to the development and effective use of chemotherapeutics. Here we describe an RNA interference (RNAi)-based strategy to characterize small-molecule function in mammalian cells. By examining the response of cells expressing short hairpin RNAs (shRNAs) to a diverse selection of chemotherapeutics, we could generate a functional shRNA signature that was able to accurately group drugs into established biochemical modes of action. This, in turn, provided a diversely sampled reference set for high-resolution prediction of mechanisms of action for poorly characterized small molecules. We could further reduce the predictive shRNA target set to as few as eight genes and, by using a newly derived probability-based nearest-neighbors approach, could extend the predictive power of this shRNA set to characterize additional drug categories. Thus, a focused shRNA phenotypic signature can provide a highly sensitive and tractable approach for characterizing new anticancer drugs.