We test this by performing 100 pyGlobOpt runs, with the grouping algorithm active (c.f. section 2.2), and taking the average. The results are shown in figures 3 and 2 as the purple lines. It is apparent that our tuned pyGlobOpt with grouping has a lower average energy than both C 0 and pyGlobOpt with grouping. However, in figure (middle) we observe unlike pyGlobOpt without grouping there is not a significant decrease in the number of unique structures. We also observe in figure (bottom) that tuned settings produce consistently the lowest average energy structures over nearly all iterations. The most impressive result from the optimal settings is the average can be seen in figure (top) the number of ULES produced outperforms all other methods with an average of 39.2 produced over 1000 iterations (0.039 ULES per iteration) which is the highest we have currently recorded. It is apparent that by combining these setting choices they have synergistically enhanced the GO algorithm's low-energy PES exploration. This has interesting applications to method development in the future and what 'optimal' algorithms will look like. We can also explore if system specific rules for exploring the PES can be found.