Selection of the WaterKit model. Different combinations of the parameters and sampling methods in WaterKit were tested for their accuracy in reproducing the GIST results obtained with MD simulations. The comparative analysis showed that optimal performance was achieved with only standard hydrogen bond anchor points (AP polar ), a nondirectional interaction model for the initial placement of spherical water molecules (i.e.: based on the Vina O_DA interaction probe), and a final energy minimization of 100 steps. Anchor points. Water molecules were either placed on only polar anchor points (AP polar ) using a predefined set of SMARTS patterns, or by including also non-polar hydrogen atoms (AP all ). Results show that AP polar seems to achieve a marginally better agreement with the with MD simulation results (Fig. ). This is not surprising given the Boltzmann weighted sampling scheme applied, since even when considering all hydrogens (AP all ) the lower energies associated to non-polar ones made them less likely to be selected for the initial spherical water placement. Spherical water placement. For the initial placement of spherical water molecules on APs, two different map models were tested, using either the one based on the explicit TIP3P water molecule with the electrostatic term, or the (O_DA) acceptor/donor probe from the Vina force field without electrostatic term. Surprisingly, the non-directional Vina O_DA probe resulted in better placements and energy predictions than the more accurate directional model (Fig. ). Likely, the lack of both hydrogen directionality and electrostatic contributions provided an advantage to the Vina O_DA model, making it less susceptible to the structural bias associated with discrete orientations of disordered hydrogen atoms (i.e.: hydroxyls and amines, Fig. ). Minimization. In the MD simulations, relatively soft restraints are applied on the target atom positions, while all water molecules free to move. Conversely, in WaterKit, both the target structure and previously placed water molecules are kept rigid during the entire process, in order to use the pre-calculated grid affinity maps. This results in a reduced modeling accuracy of the E ww contribution, with respect to the E sw , because due to the sequential layer placement, newly placed water molecules can only take into account interactions within the current layer or the underlying ones. To mitigate this, we added a final minimization step (Fig. ) allowing both water molecules and target atoms to relax within similar restraints used for the MD simulations. While this step allows only minimal adjustment of the water coordinates, it can remove possible strains added by the progressive water layers placement. In fact, this step improved the quality of water-water interactions (E ww ) as well as the entropic components (TS trans and TS orient ), resulting in higher r 2 and lower RMSD values. Results show that 75 to 100 steps were sufficient to achieve optimal performance, while minimal to no gain was found beyond that (Fig. and Fig. for more details).