Structure-based clustering is commonly used to identify correct protein folds among candidate folds (also called decoys) generated by protein structure prediction programs. However, traditional clustering methods exhibit a poor runtime performance on large decoy sets. We hypothesized that a more efficient partial clustering approach in combination with an improved scoring scheme could significantly improve both the speed and performance of existing candidate selection methods.