MicroRNAs (miRNAs) are short endogenous noncoding RNAs that bind to target mRNAs, usually resulting in degradation and translational repression. Identification of miRNA targets is crucial for deciphering functional roles of the numerous miRNAs that are rapidly generated by sequencing efforts. Computational prediction methods are widely used for high-throughput generation of putative miRNA targets. We review a comprehensive collection of 38 miRNA sequence-based computational target predictors in animals that were developed over the past decade. Our in-depth analysis considers all significant perspectives including the underlying predictive methodologies with focus on how they draw from the mechanistic basis of the miRNA-mRNA interaction. We also discuss ease of use, availability, impact of the considered predictors and the evaluation protocols that were used to assess them. We are the first to comparatively and comprehensively evaluate seven representative methods when predicting miRNA targets at the duplex and gene levels. The gene-level evaluation is based on three benchmark data sets that rely on different ways to annotate targets including biochemical assays, microarrays and pSILAC. We offer practical advice on selection of appropriate predictors according to certain properties of miRNA sequences, characteristics of a specific application and desired levels of predictive quality. We also discuss future work related to the design of new models, data quality, improved usability, need for standardized evaluation and ability to predict mRNA expression changes. 