In this study, we employ a data-driven approach to identify molecular features that most strongly influence ion selectivity for a dataset of 563 alkali metal coordinating molecular complexes and their associated binding energies. Rather than directly probing ion-channel binding energies and free energies via simulation, we glean insights from a diverse set of molecular complexes that have been experimentally studied. From this diverse set we apply machine learning (ML) procedures and identify features of the coordination environment that can energetically discriminate between alkali ion species.