Retrospective alcohol use data are prone to recall bias, a limitation that could be addressed with real-time ecological momentary assessment (EMA) tools. We aimed to (1) introduce a simple (single-click) EMA methodology for collecting real-time alcohol use data, and (2) investigate the EMA methodology's performance relative to established alcohol use data collection tools. In March-April 2021, we sampled undergraduate students (n = 84) and collected a week of alcohol use data. Participants entered their real-time drinking start times using our EMA methodology, self-reported their drinking details in daily surveys, and a subsample recorded their breath alcohol concentration (BrAC) using smart breathalyzers. We estimated the accuracy of our EMA methodology in collecting alcohol use data relative to data collected by daily surveys and breathalyzers. Overall, 199 drinking events were recorded with the EMA methodology. Numbers of drinks recorded with the EMA methodology were correlated with self-reported daily surveys (r = 0.82, p < 0.001) and BrAC readings (r = 0.69, p < 0.001). Sensitivity and specificity of the EMA methodology in detecting heavy drinking relative to daily surveys were 82% (95% CI [67%, 92%]) and 97% (95% CI [85%, 100%]), respectively. These were 74% (95% CI: [64%, 83%]) and 92% (95% CI: [85%, 96%]) for binge drinking. Similar results were found when we used breathalyzers as the reference standard test. We developed an EMA methodology for collecting real-time alcohol use data (alcohol drinking start-time, frequency, magnitude, patterns, and pace). Our findings support the utility of our EMA methodology in collecting alcohol use data among college students.