The predictive performance of traditional supervised methods heavily depends on the amount of labeled data. However, obtaining labels is a difficult process in many real-life tasks including compound screens, biomarker discovery etc. Only a small amount of labeled data is typically available for model learning. As an answer to this problem, the concept of semi-supervised learning has emerged. Semi-supervised methods use unlabeled data in addition to labeled data to improve the performance of supervised methods. It is even more difficult to get labeled data for data mining problems with structured outputs since several labels need to be determined for each example. Multi-target prediction (MTP) is one type of a structured output prediction problem, where we need to simultaneously predict multiple variables. Despite the apparent need for semi-supervised methods able to deal with MTP, only a few such methods are available and even those are difficult to use in practice and/or their advantages over supervised methods for MTP are not clear. We will present an algorithm for learning predictive models from limited amount of labelled data that can exploit the available unlabelled data in a way to yield models with better predictive performance. We will also show some benchmark experiments to assess their predictive performance. Finally, we will illustrate and discuss their use for analysis of high content screens.