In principle, Swarm-CG can also be employed for tuning BP in higher-scale CG models (e.g. lower coarsegraining resolution, mapping more atoms into each CG bead). While demonstrating such a usage of the software is outside the scope of this paper, the workflow would remain substantially unchanged, except for: (i) the non-bonded parameters provided, (ii) the mapping file that would group multiple atoms into each larger CG bead and (iii) the number of CG beads and swarm iterations used for optimization, which could potentially be decreased to minimize execution times, without affecting the accuracy of the results. Moreover, in such a case the user would not be restricted to using an AA-MD trajectory as the reference, but also a finer CG-MD trajectory would work to this purpose (with the advantage of a speed up in the process and of an overall improved dynamical sampling). In this sense, the successive higherscale parametrizations (finer-to-coarser CG optimization) would be less computationally expensive than the first one (AA-to-CG), while the user should at the same time consider that approximations intrinsically accompany every CG step, and that in multi-step approaches accuracy is key to avoid sum of errors. Swarm-CG could also be applied to the bonded parametrization of polarized CG models or elastic networks used in CG models of proteins (e.g. MARTINI), via limited additional code developments. The code has been developed for immediate usage with the MARTINI CG force field (it works with both explicit and implicit solvent environments), although it is easily adaptable to be used also in combination with other CG frameworks, and as said potentially across various CG scales.