Gadolinium based contrast agents (GBCA) are routinely administered for identifying active lesions in multiple sclerosis (MS). Because of the safety concerns with GBCA, alternative methods are highly desirable to identify active lesions without GBCA. We used Deep Learning, specifically multi-layered VGG16 network, to identify active MS lesions on the pre-contrast images. The network was trained using a large number of annotated multi-modal magnetic resonance image volumes (792) acquired as a part of phase 3 clinical. The DL results look quite promising as judged by the accuracy, sensitivity, and specificity of 0.729, 0.861, 0.598, respectively.