SHAMAN is a computational technique designed to identify potential small-molecule binding sites in RNA structural ensembles. It combines atomistic molecular dynamics simulations with enhanced sampling techniques, such as metadynamics, to explore the dynamic conformational landscape of RNA. The method uses probes to efficiently identify RNA pockets and ranks them based on binding free energy. Benchmarking against a set of biologically relevant systems, including large, structured riboswitches and small, flexible viral RNAs, demonstrated that SHAMAN successfully identified all experimentally resolved pockets and ranked them among the most favorable probe hotspots. This approach sets a solid foundation for future drug design efforts targeting RNA with small molecules, addressing the challenges posed by the dynamic nature of RNA molecules. The accuracy and reliability of SHAMAN in identifying binding sites across diverse RNA systems highlight its potential value in rational drug design.SHAMAN is a computational technique designed to identify potential small-molecule binding sites in RNA structural ensembles. It combines atomistic molecular dynamics simulations with enhanced sampling techniques, such as metadynamics, to explore the dynamic conformational landscape of RNA. The method uses probes to efficiently identify RNA pockets and ranks them based on binding free energy. Benchmarking against a set of biologically relevant systems, including large, structured riboswitches and small, flexible viral RNAs, demonstrated that SHAMAN successfully identified all experimentally resolved pockets and ranked them among the most favorable probe hotspots. This approach sets a solid foundation for future drug design efforts targeting RNA with small molecules, addressing the challenges posed by the dynamic nature of RNA molecules. The accuracy and reliability of SHAMAN in identifying binding sites across diverse RNA systems highlight its potential value in rational drug design.