Yet another facet of factor indeterminacy rears its ugly head: Factor rotation algorithms do not generally find the best solutions of which they are capable. But when enriched with the capacity to conduct repeated searches from random starting positions, a rotation algorithm's propensity to converge to optima that are merely local can be fashioned into a seine for catching interpretively provocative rotations of the input factors that might otherwise elude discovery.