Recent developments in data science are reshaping the exploration of the chemical space, using machine learning to probe millions of different chemical species. Although it's a case of theory ahead of experiment, that's not a topic in this paper. I surveyed a couple of cases, but it would require a paper on its own to do a fair job, given the vast number of such works for material and catalyst designing. More information on this topic can be found in these recent reviews. This paper is organized into two main sections. Section 2 reviews some key concepts and methods in computational theoretical chemistry, which will help non-specialist readers follow the case studies. Section 3 surveys twenty studies where computational chemistry predictions were later experimentally confirmed.