The number of nanoporous materials is potentially unlimited; for the metal-organic frameworks (MOFs) alone, over 90 000 structures have been synthesized and 500 000 computationally constructed . This ever-increasing amount of structures requires more efficient screening procedures as well as faster evaluation tools. To go beyond the time-consuming calculations over the whole dataset, computational chemists developed funnel-like screening procedures to reduce the need for expensive simulations and introduced machine learning (ML) models. To further improve the selectivity screening for Xe/Kr separation, the research needs to focus on designing better performing structural and energy-based descriptors.