Anchored matching-adjusted indirect treatment comparisons (ITC) or propensity score adjustment methods (e.g. matching, reweighting) leverage individual patient data (IPD) to compare treatments across trials by anchoring through a common comparator. Since anchored ITCs adjust for imbalances in prognostic factors via the common comparator, health technology agencies (HTAs) such as NICE have recommended adjusting only for treatment effect modifiers that exhibit large imbalances between studies. Although many automated and semi-automated methods (e.g. high dimensionality propensity score, random forest, Bayesian Additive Regression Trees) for variable selection are available in open-source software packages, most practical applications of these methods have focused on detecting prognostic factors rather than treatment effect modifiers, and do not factor in imbalances between studies. This study provides a general overview of common machine-learning variable selection routines often employed for prognostic applications and explore their feasibility for identifying imbalanced treatment effect modifiers. We use Monte Carlo simulation studies to evaluate the finite sample performance of several supervised machine learning algorithms, in terms of their ability to detect true treatment effect modifiers with large imbalances, bias, and efficiency. Simulations are performed using simulated IPD that include one or more of the following characteristics: a) small samples; b) data sparsity relative to the number of potential treatment effect modifiers; and c) collinearity among variables. The findings from this overview and simulation study can inform data-driven variable selection methods for HTA and regulatory submissions involving indirect treatment comparisons which require the identification of variables that are important effect modifiers and differ between studies.