||Less than 1 in 10 drug candidates that enter phase 1 clinical trials actually gets approved for human use. The high failure rate is in part due to unforeseen side effects or toxicity. A better understanding of the role of selectivity and a better insight in the off-target activities of drug candidates could greatly aid in preventing candidates to fail for these reasons. This thesis has tried to address some aspects in this challenging part of drug discovery. The use of activity-based protein profiling as presented in Chapters 2 and 3 in drug discovery and hit-to-lead optimization, and in Chapter 5 and 6 for the interaction profiling of a drug candidate, highlights the versatility and importance of this chemical biology technique. Combined with knowledge derived from biochemical assays, such as that developed in Chapter 4, ABPP can greatly aid the medicinal chemist. The recent surge in popularity of machine learning algorithms, backed by exponential growth of the amount of biological data available, holds great promise for drug discovery. Chapters 7 and 8 showed the applicability of one such algorithm, which was able to quite reliably predict interaction profiles. The challenges in finding, determining and predicting selectivity are far from solved, but, by incrementally expanding our understanding of the binding of small molecules to their (off-)targets, truly selective inhibitors might at some point become a reality or their necessity might be mitigated.