We first validate our data-driven models for predicting metal hydride thermodynamic properties, with several improvements made over our previous work arising from improved features and more training data. Next we demonstrate these models can rapidly screen 10,000s of high entropy alloy compositions to predict their hydriding properties across a diverse chemical composition space. These predictions, in combination with other simple-to-compute properties, provide a multi-dimensional objective space from which a Pareto front can be constructed and optimal materials selected. After choosing materials located on or near the Pareto front, we synthesize, characterize, and test 3 high entropy hydrides to close the materials discovery loop in the search for new, highperforming HEA hydrides.