Drug Discovery is aimed at finding novel approaches to unmet medical needs. This requires identifying and validating biological pathways and their associated molecular targets, discovering and optimizing chemical structures and running Proof of Concept trials in humans. Each step along this process is aimed at selecting a limited number of scientifically sound options from the large pool of known genes and available chemical diversity. This complex process relies on experimental approaches which yield large amounts of data, leading to major challenges in data analysis and interpretation. In this context, it is not surprising that in silico methods are being developed with the aim to accelerate and optimize the Drug Discovery process. These methods range from data mining, modeling and simulation of molecular interactions, biological networks and processes and the large scale computer-aided analysis of scientific literature and patents. The demands for such approaches will increase dramatically in the years to come, providing Drug Discovery with new ways to associate pathways and targets with diseases and select candidate drugs. This presentation will outline how in silico approaches and High Performance Computing can impact Drug Discovery through specific examples.