As the COVID-19 pandemic unfolds at an alarming rate, scientists and clinicians are desperately looking for effective countermeasures. With vaccines estimated to become available in no less than a year, and with the development of brand-new medications requiring even longer times, the focus of attention has been on the already approved drugs (or those in advanced clinical trials) that could be re-purposed against COVID-19 within much shorter time scales. In this respect, first WHO trials are already ongoing and cover a range of potential therapies, from various antivirals, to antimalarial chloroquine/hydroxychloroquine, to interferon-beta, to antibody-rich plasma from COVID-19 recoverees. A literature search we conducted shows that, in total, 42 small-molecule medications are in various forms of clinical trials and some 29 other ones have been shown to exhibit some in vitro activity (for all literature sources, see Supplementary Information, Table ). While much hopeand even popular hype -has been pinned at these studies, it should be emphasized that their outcome remains uncertain and, in the worst-case scenario, none of the currently tested drugs will prove effective. If this is so, the last-ditch effort would be to consider repurposing of other drugs -the question is, of course, which ones. Here, we use the methods of Artificial Intelligence, AI, to suggest such second-pass (or, "progeny") candidates based on their similarity to the ones already being considered (henceforth, "parent" drugs). We make such suggestions based on two measures of molecular similarity that are more advanced than the popular but inaccurate metrics such as fingerprint-based Tanimoto coefficients: (1) the linguistics-inspired Mol2Vec embedding and