Methodologies involving the GO of chemical clusters seek to sample beyond simple local optimisations (LOs) which may only provide stable local minima (LMs). LMs can differ, structurally and energetically, considerably from the global minimum (GM). Molecular cluster GO has been performed with a variety of algorithms, including basin hopping, genetic, evolutionary, and ABC algorithms. To understand complex reaction systems a sampling of energetically accessible structures will need to be performed such that the formation of a chemically relevant ensemble can be used in the calculation of properties that researchers are interested in. Extensive sampling of the structural landscape of complex chemical reactions is often a challenging task. Various non-swarm intelligence methods have been employed to explore the potential energy surface (PES) of molecular systems. Nakao et al. used graph neural networks and artificial forces to overcome barriers. Llanio-Trujillo et al. developed an evolutionary algorithm to optimize water and benzene clusters. Khalak et al. trained a machine learning model with alchemical free energy calculations to search the chemical space of protein inhibitors. Guo et al. used the variational auto-encoder method to search for low-energy conformers of organic molecules.