Thermodynamic characterization of metal oxide reduction/re-oxidation plays a vital role in material identification and optimization of chemical looping processes. This characterization requires significant data collection (spanning several hundred T, pO2, and composition (X) combinations) to appropriately sample phase space. We coupled a Compound Energy Formalism algorithm for reduction/re-oxidation thermodynamic model fitting with Bayesian Inference techniques to build an optimized data selection scheme. Using the BaxSr1-xFeO3-δ system as a proof of concept, we show that our Bayesian data selection technique required less than half (44) data points to achieve the same accuracy as a mesh grid of 100 T, pO2, and X point combinations. Further, randomly selected 44 T, pO2 and X data points only reproduced the ground truth model 5% of the time, demonstrating the power of our approach. Our method paves the way for a high-throughput active data selection process for metal oxide reduction/re-oxidation thermodynamics.