As a consequence, in silico methods have become increasingly popular in helping guide and support FBDD decisions . A variety of approaches have been employed for this task, which can roughly be separated into two categories; i) methods to identify fragment interaction sites, and ii) methods to characterise fragment binding affinities. For the former, both simple methods such as molecular docking , and more complex molecular dynamics-based approaches such as hotspot mapping or unbiased molecular dynamics (MD) with Markov-state modelling have shown success in identifying potential fragment binding sites on protein targets. In terms of characterising affinity, alchemical relative binding free energy (RBFE) methods have been particularly successful in ranking fragment affinities . Of particular note is a 2015 study by Steinbrecher et al. , which demonstrated that the FEP+ RBFE tool could be used to successfully rank fragment affinities, achieving an RMSE of 1.14 kcal/mol for 96 ligands across 8 fragment optimisation campaigns. Whilst RBFEs are a powerful and relatively cheap tool for this purpose, there are some disadvantages that limit its applicability in FBDD. Firstly, RBFEs do not give a direct measure of ligand binding affinities on an absolute scale, instead the method relies on other methods to normalise output free energy values. Often this would be done via an in vitro experimental measurement of a few select compounds, however as discussed above it can often be difficult to do so accurately for fragments and therefore is not always tractable in FBDD. The second major disadvantage is that RBFE protocols are generally developed to investigate small chemical perturbations on a given common chemical scaffold. This can have limited applicability in some FBDD campaigns, which will often investigate several fragments with different chemical scaffolds, sometimes even in different binding site locations. Nevertheless, we note some recent success in using RBFE methods for fragment linking purposes, although requiring several extra intermediate steps to achieve good results .