Drug discovery depends on identifying cryptic binding sites, yet traditional computational and experimental approaches are somewhat limited. The always-shifting properties of cryptic sites, which usually go undetectable in the frozen protein structures obtained by crystallography or cryo-electron microscopy, provide a significant challenge. Furthermore, complicating the identification process is the natural flexibility of proteins ( ) and the limited resolution of experimental instruments. Potential approaches to effectively overcome these limitations and find cryptic binding locations ( ) come from machine learning (ML). Using large databases of protein structures and binding interactions, ML models could forecast hidden sites that are not readily apparent with conventional methods. Combining molecular dynamics simulations with machine learning techniques allows one to precisely find hidden spots on proteins by recording their transient shapes ( ). Furthermore, machine learning can help to analyse large amounts of experimental data by identifying relationships and traits that would point to the presence of latent binding sites, therefore accelerating the process of developing drugs.