We extend the scope of a recent method for superimposing two molecules ( J. Chem. Phys. 2009, 131, 124126-1-124126-10 ) to include the identification of chiral structures. This methodology is tested by applying it to several organic molecules and water clusters that were subjected to geometry optimization. The accuracy of four simpler, non-superimposing approaches is then analyzed by comparing pairs of structures for argon and water clusters. The structures considered in this work were obtained by a Markovian walk in the coordinate space. First, a random geometry is generated, and then, the iterative application of a mutation operator ensures the creation of increasingly dissimilar structures. The discriminating power of the non-superimposing approaches is tested by comparing the corresponding dissimilarity measures with the root-mean-square distance obtained from the superimposing method. Finally, we showcase the application of those methods to characterize the diversity of solutions in global geometry optimization by evolutionary algorithms.