Many questions in comparative biology require that new data be collected, either to build a comparative database for the first time or to augment existing data. Given resource limitations in collecting data, the question arises as to which species should be studied to increase the size of comparative data sets. By taking hypotheses, existing data relevant to the hypotheses, and a phylogeny, we show that a method of “phylogenetic targeting” can systematically guide data collection while taking into account potentially confounding variables and competing hypotheses. Phylogenetic targeting selects potential candidates for future data collection, using a flexible scoring system based on differences in pairwise comparisons. We used simulations to assess the performance of phylogenetic targeting, as compared with the less systematic approach of randomly selecting species (as might occur when data have been collected without regard to phylogeny and variation in the traits of interest). The simulations revealed that phylogenetic targeting increased the statistical power to detect correlations and that power increased with the number of species in the tree, even when the number of species studied was held constant. We also developed a Web‐based computer program called PhyloTargeting to implement the approach ( http://phylotargeting.fas.harvard.edu ).