Crucially, in this work we demonstrate for the first time the ability of AL to cut down the size of training datasets that includes adsorption data of multiple molecules, using the adsorbate space explored earlier by Gómez-Gualdrón and coworkers as a testbed. We first approached this task on a per MOF strategy by using AL to generate the training data for each MOF (1800 MOFs in total), which resulted in 57.5% data savings. The resulting surrogate models from AL per MOF are used to generate training data for a new MLP model, which was shown to retain the original predictive performance of the original MLP by Gómez-Gualdrón and coworkers. Encouraged by the results, we then approach this AL task on a joint MOFadsorbate basis (alchemical adsorbates and 3445 MOFs). Excitingly, this approach results in drastic data savings of 99.8%. Lastly, we analyze the AL process, focusing on its selected features as the model is developed, providing insights into AL campaigns for adsorption.