Water molecules play a key role in mediating modulating the interactions occurring in biological environments, with important implications for drug design. Being able to capture their organization at the surfaces of biologically relevant sites is therefore essential to capture the forces behind binding events. The WaterKit protocol discussed here represents a proof of principle to assess if an incremental MC-based simulation can efficiently approximate the calculation of water thermodynamic properties otherwise accessible only through more expensive MD calculations. In a purely MC-based simulation, water molecules make only small incremental movements, thus taking longer time to converge. In contrast, the WaterKit protocol consists of a repeated stochastic iterative placement and refinement of discrete water molecules on suitable locations on the target structure, and built layer by layer. The hypothesis is that by building progressive hydration shell layers and by repeatedly starting over the placement, it is possible describe plausible water networks on protein surfaces, and calculate their individual thermodynamic components with acceptable accuracy more rapidly using the GIST method. For that, conventional MD simulations and WaterKit were compared. The results show that the approach of repeatedly building progressive water layers is effective at describing correct water networks, matching both MD results and experimental data. Interestingly, this confirms that by identifying the position of water molecules optimally interacting with the protein, it is possible to infer also the position of more weakly bound water molecules, which positions are influenced by them in a cascade effect.