The approximate halving of the training data by applying AL to the adsorbate space was encouraging, but arguably represents insufficient data savings for the increase in adsorbate space dimensionality that would occur if one expanded the types of adsorbates included in the training dataset to account for higher adsorbate complexity. Additionally, developing individual GP models for each MOF to generate the training data is notably demanding and tasking, as it requires performing AL on each MOF. To address this challenge, we decided to adopt a strategy where AL operates simultaneously on the adsorbate and MOF space. The underlying hypothesis was that what a model learns about adsorption in one MOF may be applicable to other similar MOFs, making exhaustive training data generation for all MOFs in a database unnecessary.