We envision AL to play a crucial role in the development of ML adsorption models by guiding the selection of adsorption scenarios that offer surrogate models the most information about adsorption behavior, thereby reducing the computational expense associated with conducting GCMC simulations to generate training data. We select specific combinations of MOFs, molecules, and conditions that contribute to the surrogate model's predictions of adsorption. AL has been demonstrated to reduce the data burden to train models that predict adsorption of specific molecules. For instance, in a previous study, Osaro and coworkers demonstrated the development of a model a few predict full pure gas isotherms for methane, nitrogen, hydrogen and carbon dioxide using few training datapoints across eleven MOFs. In another instance, Mukherjee and coworkers used AL to develop a model to predict full isotherms for methane and carbon dioxide in HKUST-1 at different temperatures. AL has additionally been employed to train a model capable of predicting adsorption behaviors for various gas pairs, including xenon-krypton, carbon dioxide-methane, and hydrogen sulfidecarbon dioxide, within a single MOF. However, the use of AL towards the development of universal adsorption models, and the extent to which it could reduce the data burden in such context, has not been explored.