The MLP previously developed by Gómez-Gualdrón and coworkers used approximately 5 million training data points derived from GCMC simulations involving 200 alchemical adsorbates across 1800 MOFs. It established strong correlations between the predictions generated by the MLP model and the adsorption results obtained from GCMC simulations and will be used as a surrogate for GCMC in this study section. In this section, we demonstrate the efficacy of AL in developing a similarly predictive model, while reducing the training dataset on a per MOF basis. We constructed a new MLP model capable of predicting the adsorption behavior of real molecules using training data originated from the surrogate GP models developed by AL for each MOF. Namely, with our first AL approach, we executed the AL process in each MOF separately, and subsequently amalgamated the training datapoints selected by AL for each of all 1800 MOFs into a unified dataset. The latter was then utilized to train a new MLP model, which was tested to predict adsorption of real molecules.