Machine learning (ML) based energy prediction models are among the most effective descriptor-based catalyst screening tools for heterogeneous reaction systems. However, their implementations are limited due to expensive data labelling, ab-initio feature evaluation and lack of universal catalyst features, i.e. beyond d-band theory. Herein, we propose an inexpensive geometric feature for application on systems beyond d-band theory, e.g. perovskites comprising of s-, p-, d- and f-block elements. We outline a workflow that inputs these features into an active learning algorithm that enables effective data labelling, whilst improving prediction accuracies of existing models. We then use batch sampling to define termination criteria and to implement time-series error forecasting for further reducing the number of expensive data labelling for training. We implement this workflow to train ML models for predicting oxygen adsorption free energy on perovskites and achieve similar, if not better, prediction accuracies as obtained from ab-initio features.