We have employed in parallel the Catalyst HypoGen pharmacophore modeling approach and the variable selection k-nearest neighbor quantitative structure-activity relationship (kNN QSAR) method to model a diverse data set of p38 mitogen-activated protein (MAP) kinase inhibitors. The HypoGen pharmacophore model, developed from a novel automated training set selection protocol, identified chemical functional features that were characteristic of the active compounds and differentiated the active from the inactive inhibitors. The kNN QSAR modeling employed topological descriptors and afforded predictive QSAR models with consistently high values of both leave-one-out cross-validated R2 for the training set and predictive R2 for the test set. The results of both modeling approaches were sensitive to the selection of the training and test sets used for model development and validation. The resulting Catalyst pharmacophore and kNN QSAR models can be used concurrently for rapid virtual screening of chemical databases to identify novel p38 MAP kinase inhibitors.