These results are compared to a basic MSMD simulation of T4L99A in a 0.5 M solution of benzene and are shown in Figure . The MD grid is contoured at 30% indicating the grid points where a benzene atom had resided for 30% or more of the simulation frames, showing that the benzene binding site was not sampled at all. This grid-based analysis could be exploited to give a rough estimate of free energy by comparing its occupancy to that of bulk solvent, in a fashion similar to other MSMD methods. Various computational studies of benzene binding to T4L99A, including long MD simulations, have shown that the binding of benzene can take tens of microseconds. In a real world setting, simulations of this time scale are impractical and expensive, further highlighting the benefits of the GCNCMC approach, namely its efficiency: An average of 34 moves at 150 ps switching time is equivalent to approximately 5 ns of simulation time (accounting for the MD steps between each GCNCMC trial). Like T4L99A, MUP1 is another protein system with an occluded binding pocket and has been used as a test system for relative binding free energy calculations. Again, we perform GCNCMC enhanced MSMD simulations using three different fragment molecules that are known to bind to MUP1, 07, 08, and 14 (Fig. ), in an attempt to predict the known binding sites starting from an unbound structure. In Figure the MD and GCNCMC occupancy grids are presented. Immediately, it is clear that basic MD fails to sample the binding pocket, and even setting the contour level of the MD occupancy grids to 1% still shows no binding. As for T4L99A, this lack of binding can be attributed to two factors: first, the binding site is completely occluded from the bulk solvent and, second, at 0.5 M, all three fragments aggregated, severely impacting the level of sampling that is achievable in the simulation. These results have positive implications for structure-based drug design in locating novel binding pockets for protein targets that may not be amenable to x-ray crystallography. Our method enhances traditional MSMD approaches which struggle to find occluded pockets. We will expand this simple use case of GCNCMC in the future.