Despite the ABC algorithm's strong performance in PES exploration premature convergence, i.e. getting stuck at a non-global minima, has been a reported issue. Upon performing several calculations for our system we observed similar phenomena. We were able to identify that the production of multiple of the same low-energy structures as the cause of our 'premature convergence'. In practice, this superfluous production of multiple low-energy structure lead to the pool that random structure were drawn from in equations 3 and 4 to be 'poisoned' by multiple of the same structure. This severely hindered the efficacy of the permutation and trigonometric mutation operators therefore causing the algorithm to under-perform. We believe this 'poisoned pool' phase becomes likely under two conditions: (1) The sampled PES is small and has LMs with deep energy wells (e.g. a small Pt NP) (2) The GO methodology used, incorporates a LO smoothing. To address this we take inspiration from a technique used for another GO algorithm whereby a k-means clustering algorithm is applied to select significantly different geometries of produced structures. However, this methodology does not scale well for multiple thousands of structures. Therefore we need a method that is able to rapidly distinguish between structures and group them should they be geometrically similar.