Cheminformatics and chemometrics approaches are effective strategies for accelerating and economizing the drug design, discovery, and development cascades. Nowadays, cheminformatics has become highly interdisciplinary with rapidly evolving high-throughput screening (HTS), computer-aided drug design (CADD) strategies, machine learning (ML), and deep learning (DL) methodologies contribute effectively. This chapter is mainly centered on cheminformatics and chemometrics in drug design and discovery as a part of pharmaceutical research, pharmacology, medical chemistry, and combinatorial chemistry. This chapter can loosely be divided into three sections. An introduction to cheminformatics and the basic understanding of this field related to drug design and discovery are elaborated in the first part. This second section provides a broad panoramica of fundamental cheminformatics attributes as well as certain particular topics, including chemical databases, data format, encoding compounds, chemical descriptors, fingerprints, graph theory, similarity analysis, cheminformatics toolkit, chemical space networks, quantum profiling of chemicals, and searching data in databases. Finally, a holistic overview of fundamental cheminformatics and chemometrics methods such as data mining, fragment-based drug design (FBDD), pharmacophore mapping, molecular docking, and virtual screening are discussed in the third section.