JNK3 signaling pathway is gaining interest due to its involvement in many neurological disorders. The purpose of this study was to explore for the first time the use of a large and diverse dataset in combination with binary QSAR methodology for predicting JNK3 activity class. Data were extracted from Aureus Pharma' AurSCOPE Kinase knowledge database and active or inactive classes were assigned to ligands based on IC50 biological activity. Two sets of 2D molecular descriptors (P_VSA and BCUT) were used to build models using different biological activity thresholds. The design of the models was preceded by the evaluation of the chemical space covered by the datasets and an assessment of its chemical diversity. The best model was found using a 100 nM IC50 threshold with surface-based P_VSA descriptors. This binary QSAR model reached an overall accuracy of 98% and a leave-one-out cross-validated accuracy of 94%. Most relevant descriptors were found to encode size and hydrophobic interactions. These derived models can be useful for screening chemical libraries in the search for new JNK3 inhibitors.