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These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off\u2010target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off\u2010target effects we focused on the hybridization\u2010dependent, especially on the miRNA\u2010like off\u2010target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization\u2010dependent off\u2010target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization\u2010dependent off\u2010target effects, this review provides an overview of all major state\u2010of\u2010the\u2010art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating <jats:italic>in silico<\/jats:italic> predictions are also presented. These methods could contribute to interpret experimental results, to minimize off\u2010target effects and hopefully to avoid off\u2010target\u2010related adverse events of small RNA therapeutics.<\/jats:p><\/jats:sec><jats:sec><jats:title>LINKED ARTICLES<\/jats:title><jats:p>This article is part of a themed issue Non\u2010coding RNA Therapeutics. 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