The premise of ligand-based drug design originates from the foundation concept of medicinal chemistry, which asserts that molecules with a high degree of structural similarity possess comparable bioactivity [41]. LBDD approaches utilize primary data on active compounds (approved drugs, published reports, etc.) to predict or generate novel drug-like compounds with similar biological effects. The inactive compounds are also beneficial for the predictions, while they can be used to identify undesirable ligand features and validate the accuracy of the computational model. In this sense, the typical workflow starts with an initial set of compounds with known potency that proceeds to a chosen computational method for the similarity search. A similarity search may be conducted using a wide range of molecular descriptors or filters. Molecular descriptors are categorized based on the search’s dimensionality. Molecular weight and log P are typical 1D descriptors, whereas topological indica­tions and fingerprints are 2D descriptors. The characteristics covered by 3D descrip­tors encompass a broad range of properties from electrostatic potential to the 3D geometry of ligand moieties. Furthermore, the 3D descriptors are integrated into pharmacophore modelling. According to IUPA C recommendations, a pharmacophore is “an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response” [42]. The ideal scenario for pharmacophore modelling is when a protein-ligand complex is co-crystallized, and the ligand shows sufficient potency and bioactivity. An additional step using Quan­titative Structure-Activity Relationships (QSAR) (see Chap. 6) or machine learning (see Chap. 4) can be added to the pipeline if a comprehensive data set of active compounds is available. The most popular metric to quantify the similarity of new compounds to the initial set is the Tanimoto coefficient [43, 44].