Diabetic retinopathy is a major cause of blindness in the world. Regular screening and timely intervention can halt or reverse the progression of this disease. Digital retinal imaging technologies have become an integral part of eye screening programs worldwide due to their greater accuracy and repeatability in staging diabetic retinopathy. These screening programs produce an enormous number of retinal images since diabetic patients typically have both their eyes examined at least once a year. Automated detection of retinal lesions can reduce the workload and increase the efficiency of doctors and other eye-care personnel reading the retinal images and facilitate the follow-up management of diabetic patients. Existing techniques to detect retinal lesions are neither adaptable nor sufficiently sensitive and specific for real-life screening application. In this paper, we demonstrate the role of domain knowledge in improving the accuracy and robustness of detection of hard exudates in retinal images. Experiments on 543 consecutive retinal images of diabetic patients indicate that we are able to achieve 100% sensitivity and 74% specificity in the detection of hard exudates.