It is not feasible to explore the entire chemical space experimentally, due to the immense number of permutations possible with each element in the periodic table. According to one estimate by Walsh and coworkers, the number of possible ternary phase inorganic materials that can be synthesized out of 103 elements in the periodic table is greater than 32 million, even after constraining this space by imposing charge neutrality and electronegativity rules . However, it is possible to explore a greater fraction of this space computationally, in order to narrow down possible candidates before synthesizing them in a lab. This approach towards materials discovery has had a significant impact in fields as diverse as catalysis , Li-ion batteries , thermoelectrics , organic light-emitting diodes , and transparent conducting oxides . Within the field of catalysis, there have been high-throughput computational studies that have aimed to discover new OER catalysts. For example, Xu and coworkers utilized a bandcenter descriptor to screen 3d spinel oxides for OER activity . Ulissi and coworkers screened 2600 equimolar Ircontaining bimetallic oxides for acid-stable OER catalysts and identified 14 possible candidates predicted to be stable under acidic conditions . Nørskov and coworkers screened 47814 nonbinary metal oxides in the Materials Project and identified 68 possible acid-stable OER catalysts . In each of these studies, computational screening was used to predict the stability or activity of OER catalysts. However, to the best of our knowledge, no study has successfully utilized a highthroughput computational screen to discover novel, highly-active OER catalysts that have been experimentally demonstrated to compete with current benchmark catalysts reported in the literature. In this paper, we discuss a high-throughput computational workflow that enabled us to achieve this goal.