This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE) . We participate in Task 1 and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glassbox approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multilingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs in Task 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a lightweight alternative to the neural-based models.