CADD has led to the identification and development of many drugs used in the clinic and clinical development. Figure shows chemical structures of drugs in clinical use and clinical development where CADD methods have contributed to their identification or development. In the last two decades, substantial improvements to structure-and ligand-based drug design methods developed in CADD have been described, many of them driven by AI and its subfields ML and deep learning (DL), as recently discussed in several review papers and special issues. For instance, in structure-based drug design (SBDD), the prediction of three-dimensional structures with the AlphaFold2 neural network has generated the most complete and accurate picture of the human proteome, even highlighting its applications in cases where not similar structure is known. Other notable applications of DL are predictions of chemical reactions , synthesis automation and de novo design. The goal of the review is to discuss recent progress on selected concepts, resources, methodologies, and applications of CADD. Because of the broad scope of CADD, this manuscript is not meant to be a comprehensive review of the subject. It discusses progress on representative concepts, resources, and applications of CADD that are part of multidisciplinary efforts to advance drug discovery. Throughout the manuscript, we emphasize public resources broadly available to the scientific community. In this regard, we highlight open science. The manuscript is organized into six sections. After this introduction, the next section analyzes the role of bioactivity data in CADD and discusses advances and opportunities in SBDD and ligandbased drug design (LBDD). Section 3 addresses the chemical space and chemical multiverse concept to analyze content and diversity of chemical libraries. Emphasis is placed on constellation plots, which are based on the analog series concept, as a visual representation of the chemical multiverse. Section 4 explores exemplary and recent applications of CADD to identify hit and lead compounds. Therein, we introduce the concept of ViSAS: Virtual Screening of Analog Series, an implementation built upon the analog series formalism by Bajorath et al. designed to expand bioactive molecules from the screening of chemically related compounds in ultra-large databases. Section 5 describes recent advances in the development and application of extended similarity methods. Section 6 presents summary conclusions and perspectives.