Classifying URLs is essential for different applications, such as parental control, URL filtering and Ads/tracking protection. Such systems historically identify URLs by means of regular expressions, even if machine learning alternatives have been proposed to overcome the time-consuming maintenance of classification rules. Classical machine learning algorithms, however, require large samples of URLs to train the models, covering the diverse classes of URLs (i.e., a ground truth), which somehow limits the applicability of the approach. We here give a first step towards the use of Generative Adversarial Neural Networks (GANs) to classify URLs. GANs are attractive for this problem for two reasons. First, GANs can produce samples of URLs belonging to specific classes even if exposed to a limited training set, outputting both synthetic traces and a robust discriminator. Second, a GAN can be trained to discriminate a class of URLs without being exposed to all other URLs classes - i.e., GANs are robust even if not exposed to uninteresting URL classes during training. Experiments on real data show that not only the generated synthetic traces are somehow realistic, but also the URL classification is accurate with GANs.