Fragment-based drug discovery (FBDD) involves screening a library of small, low-molecular-weight compounds against a target protein to identify those that bind with high efficiency and specificity. These fragments are then used as starting points for the development of more potent and selective drug candidates. Compared to traditional high-throughput screening (HTS), FBDD can efficiently cover chemical space with a higher hit rate. It requires fewer compounds to be screened and can identify binders with high ligand efficiency, which is a measure of the binding energy per atom of the ligand. In silico methods, including molecular docking, play a crucial role in FBDD. Docking is used to predict the ligand-receptor interaction modes and to identify hits by structure-based virtual screening. Computational strategies can also guide the growth of fragment hits into potent leads. Fragments are particularly sensitive to scoring problems because they are weak ligands that form few interactions with the protein. Therefore, the correct and incorrect poses can be difficult to distinguish. However, incorporating binding mode information can improve fragment docking, as it helps to sort poses by similarity to the crystallographic structure of the protein in complex with known binders. FBDD is applicable to a wide range of targets, including those considered "undruggable" by traditional methods. The versatility of fragment-based docking allows it to be used in conjunction with other biophysical methods to guide fragment growth and confirm molecular interactions between a target and a ligand .