Introduction: The identification of healthy individuals harboring amyloid pathology constitutes one important challenge for secondary prevention clinical trials in Alzheimers disease. Consequently, noninvasive and cost-efficient techniques to detect preclinical AD constitute an unmet need of critical importance. Methods: We apply machine learning to structural MRI (T1 and DWI) to identify amyloid-positive subjects. Models were trained on public ADNI data and validated on an independent local cohort. Results: Used for subject classification in a simulated clinical trial setting, the proposed method is able to save 60% unnecessary CSF/PET tests and to reduce 47% of the cost of recruitment when used in a simulated clinical trial setting. Discussion: This recruitment strategy capitalizes on already acquired MRIs to reduce the overall amount of invasive PET/CSF tests in prevention trials, demonstrating a potential value as a tool for AD screening. This protocol could foster the development of secondary prevention strategies for AD.