To perform an energy distribution comparison, as we do in figure , we run pyGlobOpt multiple times with our desired settings the user would wish to test. We also need to do the same for a control. The chosen control depends on what is being assessed. For example, the one we use in figure is a constrained random structure generator. More precisely, the control we use (C 0 ) consists of a random distribution of Pt 13 H 4 atoms inside a predefined sphere which are locally optimised with xTB (c.f. section 2.4.1 for further details). This is achieved practically within the pyGlobOpt framework by allowing only scout bees to produce structures, rendering pyGlobOpt a constrained random structure generator with the same limits imposed on normal pyGlobOpt runs. C 0 is ran 100 times for 1000 iteration a run to ensure a good average is taken. This is the control we use throughout this paper unless stated otherwise. is an average of default pyGlobOpt runs with the grouping algorithm implemented (c.f. section 2.2), and Optimal pyGlobOpt with grouping (Purple) is an average of structures using the best value for each setting from section 3.3. The dashed lines represent the average energy of all locally optimised structures produced with that methodology