We first comment the optimization case of the CG model of the water-soluble BTA. The main results of both execution modes are reported in Figure . Geometrical features of the optimized models are compared to both those calculated from AA-mapped data and from the literature CG model 30 (cf. section 6.2). Using execution mode 1, the single-pass BI coupled to distributions averaging within groups of similar geoms yielded an already appropriate set of BP at the very first step of the optimization process (Figure ), here also validated by additional 200 ns of MD simulation. For BTA, which can be considered a structurally (relatively) "simple" case with respect to the rest of the benchmark (i.e. composed of 3 core CG beads, linear side arms and a 3-fold symmetry), the BI implemented in Swarm-CG with geoms averaging proved very efficient. Optimization still reduced small mismatches in geoms distributions (Figure ) and BP scores decreased from 23.3 to 16.1 without modifying average Rg of the CG model, which was found in good agreement with AA-mapped data for both sets of BP (i.e. both errors < 5%). The set of BP which obtained the lowest score during the optimization procedure is considered as the most relevant (Figure , yellow diamonds), with respect to AA-mapped reference data, and was further validated in a 200 ns simulation. This longer simulation validates that the optimized CG model correctly reproduces both local and global geometrical features calculated from the AA-mapped trajectory. BP optimization converged within 257 steps (Table ). We also tested the manually parametrized CG model of BTA in a 200 ns simulation. BP score and average Rg error for this model were 43.2 and 4.5% with respect to AA-mapped data (Figure , right plots). Both BP sets obtained via BI and optimization using execution mode 1 fixed small mismatches observed in local geometrical features of the manually parametrized model (Figure ), without substantially improving the average Rg error. Using execution mode 2, all equilibrium values for the angles between the CG beads representing the three side chains of the BTA were fixed at 180°. As expected, the BI initially produced an imperfect set of BP (Figure and cf. SI section 1.1), which were quickly tuned as the optimization approached convergence. BP scores decreased from 52.2 to 20.0. The optimized set of BP produced more "loosely" adjusted overlap of the distributions for some angle groups that used equilibrium values at 180° (Figure , angle groups 7, 8 and 9), providing increased flexibility of the CG molecular model with respect to the optimized BP obtained via execution mode 1 and AA-mapped data (i.e. average Rg increased just by 0.65 Å, or 7 points, reaching 10.1 Å). BP scores first decreased slowly during optimization cycle 1, then only angles distributions were further optimized during cycle 2 and all BP were refined during cycle 3 using longer simulation times. At start of cycles 2 and 3 the swarm is reinitialized around the best scored set of BP obtained in previous cycles, using calibrated variations (cf. section 2.3) which allowed to escape a local minima of the objective function and produced the fluctuations of BP scores observed after steps 95 and 160. BP optimization converged within 209 steps. The selected maximum number of optimization steps is reduced compared to execution mode 1 because equilibrium angle values are provided by the user, reducing the dimensionality of the problem. It is worth re-underlining that in this case (mode 2), the BTA folding is not pre-encoded in the CG model as bonded terms, but it is exclusively a consequence of the spontaneous collapse of the molecule in the solvent (hydrophobic effect, bead-bead interactions), which, in a sense, is more physically correct. However, it is also worth noting that such a comparison between mode 1 and mode 2 shows that the two modes work substantially the same in these cases, demonstrating that the behavior of these CG models is mainly controlled by the non-bonded interactions between the CG beads in the models more than by the bonded terms, so that the difference between the 2 execution modes is globally negligible in this case (see Figure ).