To attribute a score to the BP set of a CG model (namely, how good/bad this performs compared to the reference AA model), the reference AA trajectory is first mapped to its CG representation to generate a "target/reference" AA-mapped trajectory, that the optimized CG model aims at reproducing. In this perspective, the geometrical features of the CG model can be evaluated by comparing the CG-MD trajectory to the AA-mapped, on two scales: (i) "global" structural molecular properties, e.g. the radius of gyration (Rg) and solvent accessible surface area (SASA) and (ii) "local" conformation and flexibility, which can be assessed via the distributions of geoms. Since multiple sets of BP can produce similar Rg or SASA values, it is not possible to directly use such global structural properties as feedback for the optimization process, as the results would be locally inaccurate. Therefore, Swarm-CG uses a scoring function based on the differences between the corresponding geoms distributions obtained from the CG and AA-mapped trajectories (reported in Figure ). Differences are evaluated using the Earth Mover's Distance 76 (EMD, aka Wasserstein metric), which solves the optimal transport problem 77 to quantify the amount of "work" necessary to transform one distribution into another. The set of BP selected by Swarm-CG as the outcome of the optimization process is the one that minimizes the scoring function, while Rg and SASA are monitored during the execution and ultimately used for a posteriori model validation.