To further validate the predictive power of the selected MOFs, a final GPR model was trained using this core set and the eight most selected pressure points across the entire dataset. The resulting model achieved a strong predictive performance with an 𝑅² of 0.951 and a MAE of 5.11 cm³ (stp)/g framework. Overall, this work highlights the value of employing multiple selection strategies to derive a consensus set of MOFs for predictive modeling. The case study on methane adsorption underscores how a carefully curated set of MOFs can facilitate the development of highly accurate models while minimizing redundancy in the training data.