Herein, we attempt to improve the minimization and local minima search based on ANI-ML potentials by introducing a strategy that generates multiple conformations, clusters them according to RMSD, and refines the representative conformer in each cluster. This multi-conformer approach broadens the search space and reduces the likelihood of the optimization becoming trapped in local minima, particularly in non-smooth regions of the PES. We start with small systems as a case study to overcome non-smooth regions, demonstrating that our conformer generation algorithm allows for successfully reaching local minima. Then, we extend our approach to a larger dataset, where bioactive conformations are generated using ANI-ML potentials, and evaluate its performance on this broader scope.