Massive molecular testing for COVID-19 has been pointed as fundamental to moderate the spread of the disease. Pooling methods can enhance testing efficiency, but they are viable only at very low prevalences of the disease. We propose Smart Pooling, a machine learning method that uses sociodemographic data from patients to increase the efficiency of pooled molecular testing for COVID-19 by arranging samples into all-negative pools. We show efficiency gains of 42% with respect to individual testing at disease prevalence of up to 25%, a regime in which two-step pooling offers marginal efficiency gains. Additionally, we calculate the possible efficiency gains of one- and two-dimensional two-step pooling strategies and present the optimal strategies for disease prevalences up to 25%. We discuss practical limitations to conduct pooling in the laboratory.