Whilst alchemical binding free energy calculations therefore can play a role in predicting antibiotic resistance, the method currently works best when the target protein is small and the magnitude of the change in the binding free energy large, as is the case for S. aureus DHFR and trimethoprim . For this system it has also been shown that it is possible to reduce the length of the simulations yet further but still maintain an accurate qualitative prediction . Taking all this together, we appear to have probed the limits (for now at least) of using RBFE methods to predict antibiotic resistance de novo. Interestingly, unlike the majority of other applications of RBFE, one can tolerate large, estimated errors since we are ultimately only interested in the final binary classification of resistant or susceptible. A second and related application for RBFE is reducing the likelihood of a lead compound developing resistance by providing information during the development process of the likely mutations that could confer resistance and we hope to explore this in future work.