Hydrogen is a promising energy carrier that has significant potential to decarbonize economic sectors that produce "difficult-to-abate" emissions, including heavy duty transportation (maritime shipping, rail, long-haul trucks), aviation, high temperature industrial applications (chemicals, steel making), seasonal grid storage, etc. Yet the lack of the demonstrated commercial progress to-date mainly stems from significant technical challenges related to the storage, transportation, and generation of green hydrogen. In terms of hydrogen storage, inter-metallic compounds and their substituted alloys have long been studied as promising candidates because they achieve hydrogen volumetric densities on par with or exceeding compressed gas (e.g., 700 bar H 2 ) at significantly lower pressures. While lighter weight complex hydrides have been intensively investigated in the context of light duty vehicle applications, high performance but heavier metal alloy-based hydrides could be critical in stationary applications where weight of the hydride bed is of less concern, i.e. stationary and seasonal grid storage, hydrogen compression, etc. While exploration of intermetallic and alloy hydrides in novel structural/chemical space has traditionally been an intuitive process and guided by manually derived design rules and thermodynamics models (see Ref. 18 and references therein), recent years have also seen a "big-data" approach to hydride discovery. With this strategy, (explainable) compositional machine learning models have been trained on experimentally collected hydride thermodynamic data, then applied to high-throughput screening novel composition spaces to search for possible materials exhibiting desired hydride thermodynamics. This efficient modeling capability becomes increasingly critical when considering hydrides based on high entropy alloys (HEAs), a relatively nascent class of hydrides first highlighted for their outstanding volumetric capacities arising from exceptionally large hydrogen to metal (H/M) ratios. The combinatorial explosion of compositional design space in equimolar HEAs alone is simply too large for brute-force experimental search or modeling with compute-intensive methods like density functional theory (DFT). While experimental validation of ML-predicted hydride thermodynamics has indeed been demonstrated, and the space of experimentally investigated HEA hydrides is ever growing, only a small fraction of possible compositional space has been explored. Furthermore, a perspective on the possible upper bounds of HEA hydride performance remains obtuse.