Simon et al. published one of the first articles on an ML-assisted screening approach for the separation of a Xe/Kr mixture extracted from the atmosphere. Their model's performance was highly relying on the Voronoi energy, i.e., an average of the interaction energies of a xenon atom at each Voronoi node. To rationalize this increase in performance, this Voronoi energy can be regarded as a faster proxy for the adsorption enthalpy. This Voronoi sampling was much faster than a standard Widom insertion, but also much less accurate. Therefore, we recently developed a more effective alternative, a surface sampling algorithm (RAESS) using symmetry and non-accessible volumes blocking to speed up the calculation of relevant interaction energies within a porous framework. Recently, Shi et al. used an energy grid to generate energy histograms as a descriptor for their ML model, providing an exhaustive description of the infinitely diluted adsorption energies. All the approaches described above can accurately predict the low-pressure adsorption (i.e., in the limit of zero loading), but are not suitable for prediction of adsorption in the high-pressure regime, when the material is near saturation uptake. While this later task is routinely performed by Grand Canonical Monte Carlo (GCMC) simulations, there is a lack of methods at lower computational cost for high-throughput screening. To better frame our challenge, in this work we are essentially trying to predict the selectivity in the nanopores of a material at high pressure, where adsorbates are interacting with each other, while only having information on the interaction at infinite dilution. The comparison between the low and high pressure cases provides clarifications on the origin of the differences in selectivity values. For some materials, selectivity could drop when increasing the pressure in the Xe/Kr separation application. And, this was mainly attributed to the presence of different pore sizes and potential reorganizations due to adsorbate-adsorbate interactions. In this article, we develop a new adsorption energy sampling technique using a gridbased approach. Moreover, we perform a statistical characterization of the pore size and energy distributions to inform the model on a potential selectivity drop. By combining these two approaches, we introduce a set of useful ML descriptors for fast and accurate ambientpressure selectivity prediction, and we highlight its performance in the case of xenon/krypton separation for the CoRE MOF 2019 database .