Computational methods are well-placed to enhance the fragment-based drug discovery pipeline and complement experimental methods. A wealth of in silico methods are routinely used to identify binding sites, fragment hits, and to optimize hits to leads. Even wider adoption of computational methods in FBDD is expected as they become more efficient and accurate, further accelerating drug discovery processes. Docking is a computationally inexpensive technique that can be used to quickly generate a large variety of ligand-bound configurations and to rank these poses based on a scoring function. However, many docking algorithms neglect protein dynamics, flexibility, and often yield many false positives owing to inadequate scoring functions. Moreover, it has been shown that many docking algorithms are optimized for larger, more drug-like molecules and perform much better when using crystal structures with bound analogues. Molecular dynamics-based simulations explicitly capture protein dynamics but come with their own set of limitations. Spontaneous binding events from the solvent often occur over longer timescales (milliseconds) than can be simulated in a reasonable time frame (microseconds) and are therefore rarely observed. Ligands that bind in multiple configurations can become restricted to only one, or two, modes with large energy barriers that prevent exchange between binding modes within the timescale of a typical MD simulation. Many recent enhanced sampling methods have been developed to tackle such issues and have been applied in the context of FBDD. Mixed solvent MD (MSMD) is a flavor of classical MD simulation that aims to identify binding sites and favorable interactions in protein systems using small organic probe molecules. While MSMD has had some success in identifying binding sites and hotspots, it is plagued by time scale issues, particularly if the binding event requires a large protein conformational change, or if the binding site is particularly occluded from the solvent. The choice of organic probe adds another level of complexity; ideally a range of probes would be selected to cover a variety of interaction types, however, some probes can aggregate, leading to the formation of clusters or even phase separation, limiting sampling at the protein interface by reducing the effective concentration of the system. Some protocols make use of artificial repulsion forces between ligand pairs to maintain a well mixed solution in simulation; however, this introduces artifacts and in theory could prevent cooperative binding. Others use fully miscible probes, thereby avoiding the issue altogether, but this limits the probe set available and thus restricts the chemical space that can be sampled. Similarly to XRC, structure-based computational methods also generate information on how a ligand binds in terms of its atomic positions. Ligands that bind in multiple configurations often produce ambiguous electron densities, and simulation methods can help to resolve these binding modes and generate an ensemble of stable configurations. Binding Modes of Ligands Using Enhanced Sampling (BLUES) is one such method that aims to identify different stable binding modes within a binding site. Similar to the present study, BLUES uses nonequilibrium candidate Monte Carlo (NCMC) to fully decouple a bound ligand from a binding site before applying random translational, rotational, and dihedral adjustments to the ligand, followed by recoupling. While some degree of prior knowledge of the binding site is required, BLUES has successfully identified multiple known binding modes of fragments in T4-lysozyme and soluble epoxide hydrolase. Finally, when a binding mode is known, alchemical free energy calculations may be used to predict the affinity of the molecule to its target. Affinity prediction is very important and, when performed correctly, is the most accurate and reliable way to rank a series of ligands. Relative binding free energy (RBFE) calculations provide a means of predicting the free energy difference between two closely related ligands by perturbing one or two functional groups into another. Absolute binding free energy (ABFE) calculations, on the other hand, calculate the overall binding affinity of a molecule to a target and are generally more useful when ranking ligands with different scaffolds; a retrospective study by shows the applicability of this method in the context of FBDD. However, for both methods, there is a requirement for high quality structural data from either experiment or computation, prior knowledge of the binding modes, and, in the case of ABFEs, a series of user-defined restraints to maintain the integrity of the complex as the ligand is decoupled. These restraints often require a large degree of user input and the wrong choice can noticeably affect the simulation. In our experience, weakly bound fragments, that may try to unbind, even in fully interacting states, test the robustness of these restraint protocols. Sampling limitations including the movement of protein side chains and tightly bound water molecules have continued to be the source of further development. Grand Canonical Monte Carlo (GCMC) simulations have been routinely used in recent years to simulate the grand canonical (µVT) ensemble, allowing the number of molecules in the system to fluctuate while keeping the overall chemical potential (µ) of the system constant. In practice, GCMC uses trial 'insertion' and 'deletion' moves to vary the number of molecules in the system; these trial moves are then subjected to a Monte Carlo test and are accepted or rejected according to the equilibrium properties of the system. Various applications of GCMC include sampling buried water molecules in protein-ligand binding regions, to validate crystal water positions, predict favorable water sites, and calculate the free energies of water networks. Furthermore, water-based GCMC simulations are now also commonplace in free energy calculations, such as in the popular FEP+ software, as a means of improving water sampling while an alchemical change is applied to a molecule of interest. A more recent study investigated the application of nonequilibrium candidate Monte Carlo (NCMC) to GCMC. In the context of GCMC, the addition of NCMC means that the insertion or deletion of molecules can occur gradually over a series of alchemical states such that the molecule binds with an induced fit mechanism allowing the system to respond to the proposed moves. When applied to water, the acceptance rate of this combined "Grand Canonical nonequilibrium candidate Monte Carlo" (GCNCMC) method was found to be significantly higher than 'instantaneous' GCMC, while also indirectly improving protein and ligand sampling during the move proposal. Implementing GCNCMC moves into a regular MD simulation means that sampling of these binding sites is improved while also propagating the dynamics of the system through time.