Although the effect of data balance and resampling techniques had been analysed for QSAR models, it had not been studied yet in the context of proteochemometrics models, even if the bioactivity datasets used in this setting are usually imbalanced. In this paper, we have tested four different combinations of data oversampling (through SMOTE) and clustering for controlling compounds similarity. While the clustering avoids overly optimistic performance estimates, it could introduce more data imbalance (in the form of splittings having proteins with mostly active or inactive compounds). Despite this potential conflict between the resampling and the clustering, we found that resampling was useful to improve the model behaviour and performance.