Creating bespoke force fields on-the-fly adds a lot of new torsion parameters, which in turn requires generation of new QM reference data for training them. Generation of new QM data is time consuming, and a possible alternative for faster parametrization, without too much loss in accuracy, can be a semiempirical method, or a couterpoise corrected method with minimal basis, or machine learning potentials. Here we checked the performance of the semiempirical method GFN2-XTB, Grimme's 3-corrected Hartree-Fock method (HF-3c), and two recent machine learning potentials, AIMNET2 and MACE-OFF23, the ML potentials which have demonstrated accuracy very close to the level of DFT that they were trained on. Only single point energies were evaluated here, as in the comparisons above.