As a specific use case, we first focus on the discovery of new solar thermochemical (STCH) water splitting oxides, widely considered a promising route for renewable hydrogen production. DFT investigations play a critical role in understanding these materials and helping to guide experiments, but can only be accomplished on a limited scale. Due to the complexity of the associated vacancy defect calculations using DFT, approximately one year's work was required to build our training database (∼ 200 host oxide compounds consisting of ∼1500 unique defects). Our highly generalizable dGNN model then extends upon capabilities of previous work that requires carefully hand-engineered features in specific crystal systems (e.g., perovskites) and obtains a similar expected mean absolute error in oxygen vacancy formation enthalpy (MAE < 450 meV) in unseen compounds (assuming its cations are represented in the training data). Depending on the model's defect predictions, oxide stability, and the stringency of these down-selection criteria, we then narrow down a small number of priority candidates for experimental efforts from 10,000s of possible MP oxides (among which are known STCH oxides we "re-discover" through our screening procedure).