La-BTC was predicted by the ML model to have an ideal ethane/ethylene selectivity of 8.6, but the experimental selectivity was measured to be 1.1. The high selectivity predictions were primarily driven by an underprediction of ethylene uptake, suggesting that electrostatic effects are not being accurately learned by the ML model in this low-data regime. An alternative approach in the future might be to incorporate descriptors from molecular simulation or density functional theory that are derived from the electrostatic environment in the pore of the MOFs. While the experimental value is significantly lower than predicted, it does indicate the MOF has ethane selectivity, suggesting that the models are useful for qualitative ethane-selectivity screening. The GCMC simulation selectivity of 1.05 at 101 kPa is closer to the experimental selectivity, suggesting that classical simulations are still useful for many systems. However, it is challenging to predict a priori where simulations will maintain good fidelity to experiment. In addition to using new descriptors and physical constraints, the ML model performance could be improved with the inclusion of more data in the training set from an even more diverse set of MOFs. Active learning frameworks are especially useful for this task and provide an opportunity to build upon the current work by augmenting the dataset with additional experiments. Repeat measurements on the structures already in the dataset would also be beneficial by providing information on experimental variability.