it is apparent that the number of unique structure produced stalls after around the 300th iteration. This occurs because the standard pyGlobOpt algorithm becomes trapped as the pool its employed and onlooker bees take structures from is made up of very similar structures. Therefore the only new structure being introduced are those generated randomly with scout bees. One could argue that this is inconsequential if it finds the GM and stops there. However we observe that the average lowest energy structure found for such runs lies more than 0.2 eV above the GM. In contrast, pyGlobOpt with grouping was able to achieve an average lowest energy of less than 0.1 eV above the GM and indeed even C 0 outperforms standard pyGlobOpt in this metric achieving an average lowest energy of 0.17 eV above the GM. This indicates that standard pyGlobOpt has converged too soon and is getting stuck, whereas the inclusion of clustering is able to consistently find the lowest energy structures of all three methods.