People experiencing homelessness are often considered frequent healthcare users. Although their service use is not uniform, it can be difficult to identify the highest-cost users without access to comprehensive cost data. This study validated a set of algorithms that apply healthcare encounter data to identify high-cost users among adults with a history of homelessness. Administrative healthcare cost data were compared across common frequent user definitions for emergency department (ED) visits and hospitalizations. Sensitivity, specificity, positive predictive values, and negative predictive values were derived for a set of seven algorithms. Twenty-three percent of the cohort was high-cost users. Optimal algorithms to identify high-cost users were ≥1 hospitalization with 78% sensitivity and 96% specificity and ≥1 hospitalization or ≥6 ED visits with 82% sensitivity and 89% specificity. The positive predictive values indicate that 85% of people with ≥1 hospitalization in a year and 69% of people with ≥1 hospitalization or ≥6 ED visits in a year were correctly classified as high-cost users. This study offers a straightforward method to identify high-cost users among adults with a history of homelessness. The optimal algorithms can be used to inform resource planning and service evaluation to ensure high-needs groups receive appropriate and tailored interventions.