Previous research on many-item Best Worst tasks has shown that Sparse BW designs have generally outperformed Express BW designs, especially regarding out-of-sample predictions. Recently, a suggestion was made to improve Express BW designs by including a small, fixed number of items [3-5] across all respondents. We undertake this research to ascertain whether item seeding results in better out-of-sample predictions than traditional Express BW designs have without this seeding.