MOTIVATION: The computational search for novel miRNA precursors often involves some sort of structural analysis with the aim of identifying which type of structures are prone to being recognised and processed by the cellular miRNA-maturation machinery. A natural way to tackle this problem is to perform clustering over the candidate structures along with known miRNA precursor structures. Mixed clusters allows then the identification of candidates that are similar to known precursors. Given the large number of pre-miRNA candidates that can be identified in single-genome approaches, even after applying several filters for precursor robustness and stability, a conventional structural clustering approach is unfeasible. RESULTS: We propose a method to represent candidate structures in a feature space which summarises key sequence/structure characteristics of each candidate. We demonstrate that proximity in this feature space is related to sequence/structure similarity, and we select candidates which have a high similarity to known precursors. Additional filtering steps are then applied to further reduce the number of candidates to those with greater transcriptional potential. Our method is compared to another single-genome method (TripletSVM) in two datasets, showing better performance in one and comparable performance in the other, for larger training sets. Additionally, we show that our approach allows for a better interpretation of the results.Availability and Implementation: The MinDist method is implemented using Perl scripts and is freely available at http://www.cravela.org/?mindist=1. CONTACT: backofen@uni-freiburg.de.