Performance in terms of finding the lowest energy is improved by increasing the maximum number of energy evaluations. However, the computational cost also grows significantly (see . We should note that the computational complexity of the Gaussian process regression is O(N 3 ), where N is the number of evaluations. The run time analysis on BOA was performed on a desktop running Fedora 28 with an Intel Core i7-6700 operating at 3.40 GHz, and 32 GB of RAM. A single core was used for MMFF94 energy evaluation and driving the torsion angles. All cores were used in the GPyOpt operations. The time did not include the time to read input molecules or write the conformers to disk. It took about 7 ms to update the torsions and evaluate the energy 100 times in a molecule with six rotatable bonds. Hence, the computational time is dominated by the operations within GPyOpt. This is not surprising as we chose a relatively fast energy function, i.e. MMFF94. If the energy function was replaced by a more accurate but computationally more expensive method, such as a quantum mechanical method, we expect that the Bayesian optimization algorithm would become a more competitive search strategy than those used by other stochastic search methods.