In this work, we focus on multi-objective reaction optimization under noisy conditions using Euclidian expected quantile improvement (MO-E-EQI) to deal with heteroscedastic noise. We first conduct an in silico study comparing MO-E-EQI with other recent MOBO algorithms under different noise structures and magnitudes. Then MO-E-EQI is implemented in an experimental study to guide the optimization of a real-world chemical reaction.