Personalised medicine aims to match the right drug(s) to the right patient. However, this challenge is largely unsolved. Several research groups have focused on different aspects of the challenge, ranging from generating drug screening/perturbation datasets and deriving clinically-relevant insights to testing a variety of ML approaches. While a large fraction of the literature has used gene expression or -omics data as input to the ML models, more recently, other approaches leveraging a combination of gene expression and image features extracted from microscopy experiments have been applied demonstrating that the data types provide complementary information.