In the post-genomic era, one of the key challenges for drug discovery consists in making optimal use of comprehensive genomic data to identify effective new medicines. Chemogenomics addresses this challenge and aims to systematically identify all ligands and modulators for all gene products expressed, besides allowing accelerated exploration of their biological function. Computational chemogenomics focuses on applications of compound library design and virtual screening to expand the bioactive chemical space, to target hopping of chemotypes to identify synergies within related drug discovery projects or to repurpose known drugs, to propose mechanisms of action of compounds, and to identify off-target effects by cross-reactivity analysis. Both ligand-based and structure-based in silico approaches, as reviewed in this book, play important roles in all these applications. Computational chemogenomics is expected to increase the quality and productivity of drug discovery and lead to the discovery of new medicines.