Deep　learning　based　radiomics　have　made　great　progress　such　as　CNN　based　diagnosis　and　U-Net　based　segmentation.　However,　the　prediction　of　drug　effectiveness　based　on　deep　learning　has　fewer　studies.　Choroidal　neovascularization　(CNV)　and　cystoid　macular　edema　(CME)　are　the　diseases　often　leading　to　a　sudden　onset　but　progressive　decline　in　central　vision.　And　the　curative　treatment　using　anti-vascular　endothelial　growth　factor　(anti-VEGF)　may　not　be　effective　for　some　patients.　Therefore,　the　prediction　of　the　effectiveness　of　anti-VEGF　for　patients　is　important.　With　the　development　of　Convolutional　Neural　Networks　(CNNs)　coupled　with　transfer　learning,　medical　image　classifications　have　achieved　great　success.　We　used　a　method　based　on　transfer　learning　to　automatically　predict　the　effectiveness　of　anti-VEGF　by　Optical　Coherence　tomography　(OCT)　images　before　giving　medication.　The　method　consists　of　image　preprocessing,　data　augmentation　and　CNN-based　transfer　learning,　the　prediction　AUC　can　be　over　0.8.　We　also　made　a　comparison　study　of　using　lesion　region　images　and　full　OCT　images　on　this　task.　Experiments　shows　that　using　the　full　OCT　images　can　obtain　better　performance.　Different　deep　neural　networks　such　as　AlexNet,　VGG-16,　GooLeNet　and　ResNet-50　were　compared,　and　the　modified　ResNet-50　is　more　suitable　for　predicting　the　effectiveness　of　anti-VEGF.Clinical　Relevance　-　This　prediction　model　can　give　an　estimation　of　whether　anti-VEGF　is　effective　for　patients　with　CNV　or　CME,　which　can　help　ophthalmologists　make　treatment　plan.　©　2020　IEEE.