Over the last decade, we witnessed the prolifera-tion of several machine learning algorithms capable of solving different tasks for the most diverse applications. Often, for an algorithm to be effective, significant human effort is required, in particular for hyper-parameter tuning and data cleaning. Recently, there have been increasing efforts to alleviate such a burden and make machine learning algorithms easier to use for researchers with varying levels of expertise. Nevertheless, the question of whether an efficient and fully generalizable automated Machine Learning (autoML) framework is possible remains unanswered. In this paper, we present autoAD, the first autoML framework for unsupervised anomaly detection. By leveraging a pool of different anomaly detection algorithms, each one coming with its own hyper-parameter search space, our framework automatically selects the best performing ap-proach, while determining an optimal configuration for its hyper-parameters on a given dataset. Our extensive experimental evaluation, conducted on a rich collection of datasets, shows the substantial gains that can be achieved with autoAD compared to state-of-the-art methods for unsupervised anomaly detection.