We developed 1800 GP models for MOFs using AL, necessitating a separate prediction case for each MOF when making predictions for other alchemical molecules. We leverage the datapoints used to train the final GP models at the 0.05 mol/kg uncertainty level for each of the 1800 MOFs to produce a new MLP model. All these datapoints were collected into one single training data featuring 2.1 million data points (57.5% data savings relative to the 5 million used to train the original MLP). Next, we utilized TensorFlow to train a new MLP model while optimizing its associated hyperparameters (details can be found in the SI), which was used to predict the adsorption of real molecules within different MOFs, as done previously by Gómez-Gualdrón and coworkers . Figure shows a comparison between our GP-informed MLP and the GCMC simulations.