Entity resolution is the task of identifying entities that refer to the same real-world object. It has important applications in the context of digital libraries, such as citation matching and author disambiguation. Blocking is an established methodology for efficiently addressing this problem; it clusters similar entities together, and compares solely entities inside each cluster. In order to effectively deal with the current large, noisy and heterogeneous data collections, novel blocking methods that rely on redundancy have been introduced: they associate each entity with multiple blocks in order to increase recall, thus increasing the computational cost, as well. In this paper, we introduce novel techniques that remove the superfluous comparisons from any redundancy-based blocking method. They improve the time-efficiency of the latter without any impact on the end result. We present the optimal solution to this problem that discards all redundant comparisons at the cost of quadratic space complexity. For applications with space limitations, we also present an alternative, lightweight solution that operates at the abstract level of blocks in order to discard a significant part of the redundant comparisons. We evaluate our techniques on two large, real-world data sets and verify the significant improvements they convey when integrated into existing blocking methods.