RNAi is a highly conserved intracellular mechanism, whereby dsRNA strands conduct post-transcriptional modulation of gene expression through a degradation or inhibition of the translation of target mRNA. Since its discovery in 1998, RNAi has been identified in many different organisms, including mammals, and this mechanism has provided new approaches for studies in cellular and molecular biology, functional genomics and drug discovery. siRNAs can be predicted by sequence and thermodynamic features, and the wide and proficient application of RNAi relies on the ability to select the most active siRNAs from among numerous predicted molecules. Recently, the first-generation prediction algorithms based on the characteristics of siRNAs, short hairpin (sh)RNAs and micro-(mi)RNAs have been improved by the use of computational models that account for the experimentally determined activities of large numbers of siRNAs/shRNAs and miRNAs. These second-generation algorithms differ from the first-generation algorithms in the computational tools that are used for the prediction of siRNA efficacy; although these new algorithms improve the design of effective siRNAs, they do not eliminate the requirement for an experimental evaluation of the activities of siRNAs. This review reports on the most significant second-generation algorithms of siRNA and shRNA characteristics, as well as on recently designed systems for the experimental evaluation of siRNA activities.