Accurate binding mode prediction is particularly crucial when developing automated workflows for free energy calculations which can be later incorporated into more complex pipelines, e.g. active learning protocols that use alchemical calculations to inform surrogate models . For such applications one must rely on high quality docking poses, since manual user inspection is not possible in a large scale automated process. In lead optimization applications, where RBFE calculations see the majority of use, this is normally managed by assuming a constant binding mode. This allows poses to be constrained to match a crystallographically determined binding mode, however induced-fit approaches have recently been shown to deliver further accuracy improvements, particularly where homology models have been used .