Abstract: The Alzheimer’s disease (AD) therapeutic landscape is evolving rapidly. While anti-amyloid antibodies have achieved regulatory approval, their incremental clinical benefits have intensified interest in neuroinflammation as a complementary therapeutic axis. Triggering receptor expressed on myeloid cells 2 (TREM2) represents a particularly attractive microglial target, given that loss-offunction variants confer a three-fold elevation in AD risk. Although TREM2 antibodies have shown promise in preclinical studies, clinical translation is hindered by fundamental limitations: restricted blood–brain barrier (BBB) penetration (< 0.1%), prohibitive annual costs exceeding $100,000, and pharmacokinetic inflexibility that precludes dose adjustment. Here, we examine how artificial intelligence (AI)-assisted approaches have enabled the discovery of small molecule TREM2 agonists and their implications for AD treatment.