Environmental sustainability is becoming an increasing concern to research funders and institutions. The focus of research sustainability initiatives in the biosciences has largely been on the impact of wet lab research, e.g. through single-use plastic waste and energy-hungry ultra-low temperature freezers. However, computational research, particularly big data and AI/ML methods, is an increasing contributor to the environmental impact of research. Organisations like NetDRIVE and Green DiSC have been created to provide frameworks and resources to help computational researchers tackle these environmental impacts.The Green Software Foundation defines three principles for reducing the carbon emissions of software: energy efficiency, hardware efficiency, and choosing electricity sources that are less carbon-intensive. Of these, developers can typically address energy and hardware efficiency by optimising the CPU and memory usage of their software. However, efficiency considerations must be balanced with other aspects of software development, including code readability, maintainability, and limited development time. Computational biology software is often used by novices with limited training, putting additional pressure on the software developers to ensure their code is efficient. However, the developers themselves may also lack formal training and experience in identifying and tackling efficiency bottlenecks. In this presentation I will describe potential code efficiency issues that are relevant for R/Bioconductor packages, and why developers should care about them. I will present tools to profile R code, and some strategies that can be used to improve efficiency. The aim of this presentation is to start a discussion amongst Bioconductor developers about how to improve the efficiency of Bioconductor packages, and to identify priority areas for optimisation as part of my NetDRIVE fellowship.Presented at the European Bioconductor Conference (EuroBioC2026), Turku, June 2026.