We have shown meV/atom and sub-meV/atom MAEs using a limited amount of training data in two different and important practical applications of compositionally complex metals. First, we provided a single-shot learning approach to compositionally optimize the thermodynamic stability of low-density HEAs. The results were used to explain experimental observations in the failed synthesis of a phase pure AlLiMgSnZn solid solution FCC alloy. Our screening found a similar composition that reduces the energy above the hull from ∼100 meV/atom by ∼40 meV/atom and the density. In the second application, we applied our methodology to more structurally intricate FCC hydrides with more complex property predictions, i.e., the pressure-composition-temperature curves for hydrogen equilibria. After screening the energy landscape of H occupation in Pd-substituted alloys, we predicted the PCT curves to demonstrate how the model accurately captures alloy substitution effects on the plateau pressure, which is the key thermodynamic property in many hydrogen storage applications, as well as more subtle effects like plateau width and saturation capacity. Moving forward, we anticipate this approach to be useful in a variety of inorganic materials applications that have traditionally been investigated by the CE method. We envision the build-up of large-scale FCC, BCC, HCP, etc. databases, where the generalizability and flexibility of GNN models can be used for accurate predictions across large compositional spaces without explicitly accounting for, for example, sparsity of the cluster functions in CE. We envision this to impact not only structural alloy and hydride design as demonstrated in this work, but the expansive realm of applications in which high entropy materials are being investigated today. managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration (DOE/NNSA) under contract DE-NA0003525. This written work is authored by an employee of NT-ESS. The employee, not NTESS, owns the right, title and interest in and to the written work and is responsible for its contents. Any subjective views or opinions that might be expressed in the written work do not necessarily represent the views of the U.S. Government. The publisher acknowledges that the U.S. Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this written work or allow others to do so, for U.S. Government purposes. The DOE will provide public access to results of federally sponsored research in accordance with the DOE Public Access Plan. Work at the University of Nottingham is supported by EPSRC (EP/V042556/1). We acknowledge the use of the Sulis supercomputer through the HPC Midlands+ Consortium, and the ARCHER2 supercomputer through membership of the UK's HPC Materials Chemistry Consortium, which are funded by EPSRC Grants No. EP/T022108/1 and No. EP/X035859/1, respectively.