Targeting RNA with small molecules represents a promising yet relatively unexplored avenue for the design of new drugs. Nevertheless, challenges arise from the lack of computational models and techniques able to accurately model RNA systems, and predict their binding affinities to small molecules. Here, we tackle these difficulties by developing a tailored state-of-the-art approach for absolute binding free energy calculations of RNA-binding small molecules. To do so, we combine the advanced AMOEBA polarizable force field, which accounts for both accurate multipolar electrostatics and many-body effects, to the newly developed Lambda-Adaptive Biasing Force (Lambda-ABF) scheme associated to refined restraints allowing for efficient sampling. Furthermore, to capture the free energy barrier associated to challenging RNA conformational changes, we apply machine learning to identify effective collective variables in order to use them into further enhanced sampling simulations based on an evolution of metadynamics. Applying this computational protocol to a complex Riboswitch-like RNA target, we demonstrate quantitative predictions. These results pave the way for the routine application of free-energy simulations in RNA-targeted drug discovery, thus providing a significant reduction in their failure rate.