In this study, we built a computational workflow to screen for new highly active spinel oxides for OER in basic pH conditions. A computational database of ternary-phase spinel oxide materials comprised of 52 elements in the periodic table is first constructed. These elements include alkaline, alkaline-earth, transition (with the exception of artificial Tc), post-transition and some lanthanide metal elements(La, Ce, Nd, Gd and Lu) and are circled in Figure . Every possible combination of elements that could be constructed from this list was explored, for an overall spinel system AxB3-xO4 where A and B are two different metallic elements. For each system AxB3-xO4, three different values of x (x = 0.5, x = 1, x = 1.5) are chosen in order to screen for three unique compositions A0.5B2.5O4, AB2O4 and A1.5B1.5O4. Both normal spinel (ε = 0) and inverse spinel (ε = x) structures for each specific composition were explored. Once this database was created, the thermodynamic stability of these materials was assessed to isolate materials that are likely to exist in nature. Following that, the theoretical overpotential of OER on the surface of these materials was computed. We used the results of these computations to create a machine learning model to predict OER activity using electronic structure descriptors of bulk metal, bulk oxygen, surface metal, surface oxygen and adsorbate oxygen atoms as features. This allowed us to further probe the relationship between electronic structure descriptors and OER activity. The trends observed in the results of this high-throughput computational screen were applied to synthesize three catalysts, Co2.5Ga0.5O4, Co1.5Ga1.5O4 and Co1.5Al1.5O4 and characterize them for OER activity. We discovered that Co2.5Ga0.5O4 had an overpotential of 220mV at a current density of 10mA/cm 2 and a Tafel slope of 56.0 mV.dec -1 , making it a new highly active OER catalyst successfully discovered using this novel computational workflow. The success of this workflow at predicting a real catalyst highly active for OER implies that it can be adapted to discover new highly active OER materials belonging to other classes of materials as well.