Because of the heterogeneity of human tumors, cancer patient populations are usually composed of multiple subgroups with different molecular and/or histologic characteristics. In screening new anticancer agents, there might be a scientific rationale to expect some degree of similarity in clinical activity across the subgroups. This poses a challenge to the design of phase II trials assessing clinical activity: Conducting an independent evaluation in each subgroup requires considerable time and resources, whereas a pooled evaluation that completely ignores patient heterogeneity can miss treatments that are only active in some subgroups. It has been suggested that approaches that borrow information across subgroups can improve efficiency in this setting. In particular, the hierarchical Bayesian approach putatively uses the outcome data to decide whether borrowing of information is appropriate. We evaluated potential benefits of the hierarchical Bayesian approach (using models suggested previously) and a simpler pooling approach by simulations. In the phase II setting, the hierarchical Bayesian approach is shown not to work well in the simulations considered, as there appears to be insufficient information in the outcome data to determine whether borrowing across subgroups is appropriate. When there is strong rationale for expecting a uniform level of activity across the subgroups, approaches using simple pooling of information across subgroups may be useful.