Identifying therapies with potential clinical relevance is a critical task for intervening on pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic malignancy, characterized by an extremely poor prognosis and expected to be among the most prevalent lethal tumors in the coming years. Unified therapeutic options are limited because PDAC tumors are complex at the genomic, epigenetic, and metabolic levels, often involving multiple and interacting pathways orchestrated by undruggable drivers. Inter-patient heterogeneity in drug response, accompanied by high recurrent rates of chemoresistance, makes PDAC an almost incurable tumor by pharmacological means. Resource-intensive approaches need to be implemented in order to identify effective therapies along the integration of PDAC tumor heterogeneity landscape. Large-scale technologies such as high-content screening (HCS) hold promise to uncover novel cures, especially when therapy response interrogation involves the incorporation of highly physiological-competent human PDAC models such as collections of patient-derived tumor organoids (PDOs). However, systematic and unbiassed drug combination-HCS applied directly on PDOs, intended to ensure the detection of effective pairs or multidrug combinations to preclinically inform on effective drug combinatorial regimens, is still missing. This chapter proposes a PDO-guided drug-synergy image-based HCS pipeline already implemented in a proof-of-concept study for PDAC therapy development, design and validation of drug-synergy screening, using collections of PDOs.