In addition, we show that incorporating proximity information to conventional MPNN architectures offers significant performance advantages. In all but one case, PDBbind General with Random Splitting, one of the MPNN models performed as good or better than the conventional fingerprint baseline. The performance improvement was particularly significant for the D4 Diverse Docking dataset. Despite strong performance as an ensemble, the graph networks were not suited to all tasks equally, showing the importance of diverse message-passing architectures for optimal performance on multiple applications.