The identification and development of new medical uses for existing drugs (including approved, experimental, discontinued, and shelved drugs) have emerged as an increasingly attractive way to approach the cost-efficient development of new therapeutical solutions. Whereas initially new therapeutic indications of known drugs were discovered by serendipity and majorly implied the exploitation of undesired or unexpected side effects, more recently drug repurposing has been approached systematically by pharmaceutical companies and the academic sector to rescue shelved compounds and to revitalize or expand the market for proprietary drugs. Computational methods are among the core strategies for the systematic investigation of drug repurposing potential. This chapter discusses the three essential approaches for the systematic search of additional medical uses for known drugs, namely disease-, target-, and drug-centric strategies, plus their combination using network-based approximations that currently integrate advanced machine learning tools. Computer-assisted drug repurposing is currently fundamental for structuring and analyzing the large and expanding volume of biomedical data. Interestingly, a high proportion of recently reported resources for computer-guided drug repurposing are freely available to the public through web servers or open code.