The standard drug development process is slow compared to the time scale at which the SARS-CoV-2 virus emerged and mutates, and could easily last up to 15 years. This period comprises pre-clinical phases in which large numbers of virtual or physically available molecules are considered and tested, and then clinical phases in which few molecules are validated in human trials. In early phases of the drug discovery process, computational methods have been shown to help in screening and navigating through the large chemical space. Such methods should also suggest new promising ligands. However, 90% of the molecular candidates turn out to fail later, somewhere between phase I trials and regulatory approval. Therefore, using accurate computational methods to screen and filter chemical space is key to a successful and fast drug development process. With accurate computational methods, the early phases of drug discovery that usually require 3-6 years, might be reduced to a few weeks, after which pre-clinical studies could start. The RNA genome of SARS-CoV-2 encodes 29 structural, non-structural (Nsp) and accessory proteins, which are responsible for entry and uncoating, replication, and assembly. The large, multidomain transmembrane papain-like protease (Nsp3 or PLpro), the main protease (Nsp5, 3CLpro or Mpro), the RNA-dependent RNA Polymerase (RdRP or Nsp12), the nucleocapsid (N), the spike protein (S), and the human host transmembrane protease (TMPRSS2), are frequently named as potential drug targets. Due to the frequent mutations in the spike protein S, other proteins are deemed more suitable as drug targets. Since the outbreak of the COVID-19 pandemic, there has been a quest for selective, potent, and bioavailable inhibitors of the aforementioned proteins using a multitude of approaches, such as high-throughput screening, virtual screening, and drug repurposing.