A robust testing program is necessary for contain- ing the spread of COVID-19 infections before a vaccine becomes available. However, due to an acute shortage of testing kits (especially in low- resource developing countries), designing an opti- mal testing program/strategy is a challenging prob- lem to solve. Prior literature on testing strategies suffers from two major limitations: (i) it does not account for the trade-off between testing of symp- tomatic and asymptomatic individuals, and (ii) it primarily focuses on static testing strategies, which leads to significant shortcomings in the testing pro- gram’s effectiveness. In this paper, we introduced a scalable Monte Carlo tree search based algorithm named DOCTOR, and use it to generate the op- timal testing strategies for the COVID-19. In our experiment, DOCTOR’s strategies result in ∼40% fewer COVID-19 infections (over one month) as compared to state-of-the-art static baselines. Our work complements the growing body of research on COVID-19, and serves as a proof-of-concept that illustrates the benefit of having an AI-driven adaptive testing strategy for COVID-19.