Machine learning (ML) seems poised to be an important tool to predict adsorption in MOFs. However, developing ML that can comprehensively navigate the immense space formed by different MOF and molecule pairings demands a high volume of training data to achieve reliable predictions. Acquiring such large datasets can be an arduous, time-consuming, and computationally expensive task. Several ML adsorption models documented in the literature demand an extensive dataset ranging from thousands to millions of data points. Our solution, active learning (AL), circumvents this necessity.