With the accumulation of large-scale genomic data such as whole-genome RNA sequencing, copy number, and mutation profiles for tens of thousands of samples, associated with screening thousands of small molecules and other perturbagens, arises the question of how to best leverage partially overlapping datasets generated at different facilities. As research groups across the world continue to generate drug screens of variable size and quality, the need for approaches that can learn from such partially overlapping experiments and improve the signal to noise ratio emerges with increasing importance. We present an application of a Bayesian group factor analysis model, where we employ a drug-centric prior to transfer information about drugs screened in the same samples in multiple datasets. We show that joint models leveraging partially overlapping pharmacogenomic datasets from the Broad and Sanger institutes can overall improve drug signature identification.