Computational chemistry has always played a key role in anti-viral drug development. The challenges and the quickly rising public interest when a virus is becoming a threat has significantly influenced computational drug discovery. The most obvious example is anti-AIDS research, where HIV protease and reverse transcriptase have triggered enormous efforts in developing and improving computational methods. Methods applied to anti-viral research include (i) ligand-based approaches that rely on known active compounds to extrapolate biological activity, such as machine learning techniques or classical QSAR, (ii) structure-based methods that rely on an experimentally determined 3D structure of the targets, such as molecular docking or molecular dynamics, and (iii) universal approaches that can be applied in a structure- or ligand-based way, such as 3D QSAR or 3D pharmacophore elucidation. In this review we summarize these molecular modeling approaches as they were applied to fight anti-viral diseases and highlight their importance for anti-viral research. We discuss the role of computational chemistry in the development of small molecules as agents against HIV integrase, HIV-1 protease, HIV-1 reverse transcriptase, the influenza virus M2 channel protein, influenza virus neuraminidase, the SARS coronavirus main proteinase and spike protein, thymidine kinases of herpes viruses, hepatitis c virus proteins and other flaviviruses as well as human rhinovirus coat protein and proteases, and other picornaviridae. We highlight how computational approaches have helped in discovering anti-viral activities of natural products and give an overview on polypharmacology approaches that help to optimize drugs against several viruses or help to optimize the metabolic profile of and anti-viral drug.