Recent applications of multimodel methods have demonstrated their potential in quantifying conceptual model uncertainty in groundwater modeling applications. To date, however, little is known about the value of conditioning to constrain the ensemble of conceptualizations, to differentiate among retained alternative conceptualizations, and to reduce conceptual model uncertainty. We address these questions by conditioning multimodel simulations on measurements of hydraulic conductivity and observations of system-state variables and evaluating the effects on (1) the posterior multimodel statistics and (2) the contribution of conceptual model uncertainty to the predictive uncertainty. Multimodel aggregation and conditioning is performed by combining the Generalized Likelihood Uncertainty Estimation (GLUE) method and Bayesian Model Averaging (BMA). As an illustrative example we employ a 3-dimensional hypothetical system under steady state conditions, for which uncertainty about the conceptualization is expressed by an ensemble (M) of seven models with varying complexity. Results show that conditioning on heads allowed for the exclusion of the two simplest models, but that their information content is limited to further differentiate among the retained conceptualizations. Conditioning on increasing numbers of conductivity measurements allowed for a further refinement of the ensemble M and resulted in an increased precision and accuracy of the multimodel predictions. For some groundwater flow components not included as conditioning data, however, the gain in accuracy and precision was partially offset by strongly deviating predictions of a single conceptualization. Identifying the conceptualization producing the most deviating predictions may guide data collection campaigns aimed at acquiring data to further eliminate such conceptualizations. Including groundwater flow and river discharge observations further allowed for a better differentiation among alternative conceptualizations and drastic reductions of the predictive variances. Results strongly advocate the use of observations less commonly available than groundwater heads to reduce conceptual model uncertainty in groundwater modeling.