The classic approach to image matching consists in the detection, description and matching of keypoints. In the description, the local information surrounding the keypoint is encoded. This locality enables affine invariant methods. Indeed, smooth deformations caused by viewpoint changes are well approximated by affine maps. Despite numerous efforts, affine invariant descriptors have remained elusive. This has led to the development of IMAS (Image Matching by Affine Simulation) methods that simulate viewpoint changes to attain the desired invariance. Yet, recent CNN-based methods seem to provide a way to learn affine invariant descriptors. Still, as a first contribution, we show that current CNN-based methods are far from the state-of-the-art performance provided by IMAS. This confirms that there is still room for improvement for learned methods. Second, we show that recent advances in affine patch normalization can be used to create adaptive IMAS methods that select their affine simulations depending on query and target images. The proposed methods are shown to attain a good compromise: on the one hand, they reach the performance of state-of-the-art IMAS methods but are faster; on the other hand, they perform significantly better than non-simulating methods, including recent ones. Source codes are available at https://rdguez-mariano.github.io/pages/adimas.