<p>The number and types of chemical compounds are expanding at an unprecedented rate. To model existing chemicals and aid in the design of novel chemical structures, appropriate computational approaches, tailored to the goals of specific projects, have evolved over time. This review analyzes the expansion of “chemical space” by tracing the historical milestones that have shaped molecular modeling from its inception to the present day. Utilizing data from public compound databases and a systematic bibliometric analysis of peer-reviewed literature, including specialized sources like the <i>Journal of Computer-Aided Molecular Design</i>, we mapped the co-evolution of chemical data and the algorithms designed to process it. While drug discovery has historically been the primary driver of this growth, our discussion extends to other domains, including macromolecular structural space. Due to the nature of public data, this analysis focuses mostly on open-access repositories with a few mentions of proprietary industrial libraries. By examining the trends in publications, this article provides a perspective on the current state of the field and the future trajectories of molecular modeling in an era of big data and artificial intelligence.</p> Graphical abstract <p></p>