Age-related Macular Degeneration (AMD) is a leading cause of vision loss in developed countries. The evolution of neovascular activity in AMD, particularly in response to anti-VEGF treatments, is critical for effective management. This paper presents a simple but highly effective method for classifying the evolution between two consecutive Optical Coherence Tomography (OCT) B-scans, focusing on detecting changes indicative of neovascular activity. Our approach leverages state-of-the-art deep learning techniques, namely transfer learning of the DinoV2 model with extensive data augmentations, to improve the planning of individualized anti-VEGF treatment strategies. It achieves an F1 score of 0.83 on the public leaderboard of task 1 of the MARIO challenge.